{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"2class_classification_bidirectional.ipynb","provenance":[],"mount_file_id":"1TINVdx9gPO79igol5wTrrQDmFiEsETJE","authorship_tag":"ABX9TyPOyphv4el7hcLVD70Um1T1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"b4u4ZnaUw_8P"},"source":["\n","# Import libraries and modules\n","import numpy as np\n","np.random.seed(123)  # for reproducibility\n","from sklearn.model_selection import StratifiedKFold\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","%matplotlib notebook\n","import matplotlib.pyplot as plt\n","import keras\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import LSTM, GRU, Bidirectional\n","from keras.layers import Dropout\n","from keras.layers import Flatten, TimeDistributed, GlobalMaxPooling1D, GlobalAveragePooling1D\n","from keras.preprocessing import sequence\n","from keras.utils import np_utils\n","from keras import metrics\n","from keras import backend\n","\n","from pylab import rcParams\n","rcParams['figure.figsize'] = 15, 10\n","\n","def rmse(y_true, y_pred):\n","    return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis = -1))\n","\n","def sensitivity(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     sensitivity = tp / (tp + fn)\n","     return sensitivity\n","\n","# Recall is the same as the sensitivity\n","def recall(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     recall = tp / (tp + fn)\n","     return recall\n","\n","def specificity(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     specificity = tn / (tn + fp)\n","     return specificity\n","\n","\n","def precision(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     precision = tp / (tp + fp)\n","     return precision\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SDlcaLoC1M7q"},"source":["#(A) 2 class classification [A vs E]"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"id":"2G7RxRdCxJlU","executionInfo":{"status":"ok","timestamp":1619875978354,"user_tz":-330,"elapsed":1396,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"9b5217d5-ae6e-4fcf-c66a-ab282fac2267"},"source":["df=pd.read_csv(\"/content/drive/My Drive/mtech_finalyr_project/modified_Dataset/final_csv_with_shuffle_AvsE.csv\")\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","    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-8.0  -11.0    4\n","197  213.0  210.0  210.0   212.0  194.0  ...   -8.0   -9.0   -2.0   20.0    4\n","198   24.0   10.0   -9.0   -18.0   -9.0  ...  -29.0  -39.0  -33.0  -28.0    0\n","199  -20.0  -12.0   -6.0     6.0    8.0  ...   10.0   12.0    9.0    2.0    0\n","\n","[200 rows x 4097 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"id":"B7qRAmmKzvOx","executionInfo":{"status":"ok","timestamp":1619875980239,"user_tz":-330,"elapsed":1851,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"0485842e-443c-4599-f33a-b4da0adb44cb"},"source":["#converting tags into 0 nd 1\n","df.tag=df.tag.replace({4:1})\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","      <th>25</th>\n","      <th>26</th>\n","      <th>27</th>\n","      <th>28</th>\n","      <th>29</th>\n","      <th>30</th>\n","      <th>31</th>\n","      <th>32</th>\n","      <th>33</th>\n","      <th>34</th>\n","      <th>35</th>\n","      <th>36</th>\n","      <th>37</th>\n","      <th>38</th>\n","      <th>39</th>\n","      <th>...</th>\n","      <th>4057</th>\n","      <th>4058</th>\n","      <th>4059</th>\n","      <th>4060</th>\n","      <th>4061</th>\n","      <th>4062</th>\n","      <th>4063</th>\n","      <th>4064</th>\n","      <th>4065</th>\n","      <th>4066</th>\n","      <th>4067</th>\n","      <th>4068</th>\n","      <th>4069</th>\n","      <th>4070</th>\n","      <th>4071</th>\n","      <th>4072</th>\n","      <th>4073</th>\n","      <th>4074</th>\n","      <th>4075</th>\n","      <th>4076</th>\n","      <th>4077</th>\n","      <th>4078</th>\n","      <th>4079</th>\n","      <th>4080</th>\n","      <th>4081</th>\n","      <th>4082</th>\n","      <th>4083</th>\n","      <th>4084</th>\n","      <th>4085</th>\n","      <th>4086</th>\n","      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<td>-17.0</td>\n","      <td>-11.0</td>\n","      <td>-9.0</td>\n","      <td>2.0</td>\n","      <td>18.0</td>\n","      <td>37.0</td>\n","      <td>53.0</td>\n","      <td>52.0</td>\n","      <td>39.0</td>\n","      <td>23.0</td>\n","      <td>21.0</td>\n","      <td>14.0</td>\n","      <td>9.0</td>\n","      <td>-8.0</td>\n","      <td>-28.0</td>\n","      <td>-42.0</td>\n","      <td>-25.0</td>\n","      <td>1.0</td>\n","      <td>27.0</td>\n","      <td>49.0</td>\n","      <td>66.0</td>\n","      <td>69.0</td>\n","      <td>59.0</td>\n","      <td>43.0</td>\n","      <td>34.0</td>\n","      <td>23.0</td>\n","      <td>23.0</td>\n","      <td>25.0</td>\n","      <td>13.0</td>\n","      <td>...</td>\n","      <td>63.0</td>\n","      <td>72.0</td>\n","      <td>91.0</td>\n","      <td>96.0</td>\n","      <td>96.0</td>\n","      <td>86.0</td>\n","      <td>67.0</td>\n","      <td>42.0</td>\n","      <td>12.0</td>\n","      <td>-3.0</td>\n","      <td>-27.0</td>\n","      <td>-43.0</td>\n","      <td>-48.0</td>\n","      <td>-41.0</td>\n","      <td>-31.0</td>\n","      <td>-17.0</td>\n","      <td>-8.0</td>\n","      <td>1.0</td>\n","      <td>12.0</td>\n","      <td>25.0</td>\n","      <td>31.0</td>\n","      <td>27.0</td>\n","      <td>28.0</td>\n","      <td>24.0</td>\n","      <td>12.0</td>\n","      <td>1.0</td>\n","      <td>-1.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","      <td>16.0</td>\n","      <td>33.0</td>\n","      <td>48.0</td>\n","      <td>55.0</td>\n","      <td>67.0</td>\n","      <td>60.0</td>\n","      <td>46.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>196</th>\n","      <td>-459.0</td>\n","      <td>-778.0</td>\n","      <td>-981.0</td>\n","      <td>-1048.0</td>\n","      <td>-734.0</td>\n","      <td>-183.0</td>\n","      <td>478.0</td>\n","      <td>1040.0</td>\n","      <td>1381.0</td>\n","      <td>1502.0</td>\n","      <td>1458.0</td>\n","      <td>1312.0</td>\n","      <td>1109.0</td>\n","      <td>881.0</td>\n","      <td>654.0</td>\n","      <td>456.0</td>\n","      <td>264.0</td>\n","      <td>107.0</td>\n","      <td>-31.0</td>\n","      <td>-141.0</td>\n","      <td>-219.0</td>\n","      <td>-285.0</td>\n","      <td>-327.0</td>\n","      <td>-365.0</td>\n","      <td>-354.0</td>\n","      <td>-289.0</td>\n","      <td>-204.0</td>\n","      <td>-176.0</td>\n","      <td>-308.0</td>\n","      <td>-663.0</td>\n","      <td>-1026.0</td>\n","      <td>-1189.0</td>\n","      <td>-787.0</td>\n","      <td>-100.0</td>\n","      <td>651.0</td>\n","      <td>1117.0</td>\n","      <td>1304.0</td>\n","      <td>1344.0</td>\n","      <td>1276.0</td>\n","      <td>1149.0</td>\n","      <td>...</td>\n","      <td>-85.0</td>\n","      <td>-57.0</td>\n","      <td>-16.0</td>\n","      <td>17.0</td>\n","      <td>31.0</td>\n","      <td>21.0</td>\n","      <td>0.0</td>\n","      <td>-29.0</td>\n","      <td>-46.0</td>\n","      <td>-42.0</td>\n","      <td>-56.0</td>\n","      <td>-108.0</td>\n","      <td>-241.0</td>\n","      <td>-444.0</td>\n","      <td>-635.0</td>\n","      <td>-787.0</td>\n","      <td>-793.0</td>\n","      <td>-665.0</td>\n","      <td>-414.0</td>\n","      <td>-108.0</td>\n","      <td>185.0</td>\n","      <td>413.0</td>\n","      <td>522.0</td>\n","      <td>515.0</td>\n","      <td>435.0</td>\n","      <td>305.0</td>\n","      <td>174.0</td>\n","      <td>82.0</td>\n","      <td>25.0</td>\n","      <td>1.0</td>\n","      <td>-1.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>-3.0</td>\n","      <td>-8.0</td>\n","      <td>-6.0</td>\n","      <td>-3.0</td>\n","      <td>-8.0</td>\n","      <td>-11.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>197</th>\n","      <td>213.0</td>\n","      <td>210.0</td>\n","      <td>210.0</td>\n","      <td>212.0</td>\n","      <td>194.0</td>\n","      <td>162.0</td>\n","      <td>121.0</td>\n","      <td>84.0</td>\n","      <td>45.0</td>\n","      <td>11.0</td>\n","      <td>-13.0</td>\n","      <td>-20.0</td>\n","      <td>-27.0</td>\n","      <td>-32.0</td>\n","      <td>-31.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-61.0</td>\n","      <td>-72.0</td>\n","      <td>-80.0</td>\n","      <td>-85.0</td>\n","      <td>-87.0</td>\n","      <td>-93.0</td>\n","      <td>-96.0</td>\n","      <td>-97.0</td>\n","      <td>-94.0</td>\n","      <td>-82.0</td>\n","      <td>-57.0</td>\n","      <td>-18.0</td>\n","      <td>-3.0</td>\n","      <td>-11.0</td>\n","      <td>-1.0</td>\n","      <td>41.0</td>\n","      <td>120.0</td>\n","      <td>181.0</td>\n","      <td>202.0</td>\n","      <td>198.0</td>\n","      <td>195.0</td>\n","      <td>197.0</td>\n","      <td>190.0</td>\n","      <td>...</td>\n","      <td>-110.0</td>\n","      <td>-108.0</td>\n","      <td>-112.0</td>\n","      <td>-113.0</td>\n","      <td>-112.0</td>\n","      <td>-103.0</td>\n","      <td>-100.0</td>\n","      <td>-98.0</td>\n","      <td>-95.0</td>\n","      <td>-94.0</td>\n","      <td>-92.0</td>\n","      <td>-91.0</td>\n","      <td>-89.0</td>\n","      <td>-86.0</td>\n","      <td>-84.0</td>\n","      <td>-78.0</td>\n","      <td>-70.0</td>\n","      <td>-63.0</td>\n","      <td>-53.0</td>\n","      <td>-46.0</td>\n","      <td>-37.0</td>\n","      <td>-34.0</td>\n","      <td>-29.0</td>\n","      <td>-28.0</td>\n","      <td>-23.0</td>\n","      <td>-15.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-9.0</td>\n","      <td>-10.0</td>\n","      <td>-5.0</td>\n","      <td>-2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>-2.0</td>\n","      <td>-8.0</td>\n","      <td>-9.0</td>\n","      <td>-2.0</td>\n","      <td>20.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>198</th>\n","      <td>24.0</td>\n","      <td>10.0</td>\n","      <td>-9.0</td>\n","      <td>-18.0</td>\n","      <td>-9.0</td>\n","      <td>-3.0</td>\n","      <td>-2.0</td>\n","      <td>-2.0</td>\n","      <td>-9.0</td>\n","      <td>-9.0</td>\n","      <td>-14.0</td>\n","      <td>-14.0</td>\n","      <td>-24.0</td>\n","      <td>-24.0</td>\n","      <td>-12.0</td>\n","      <td>-1.0</td>\n","      <td>13.0</td>\n","      <td>31.0</td>\n","      <td>60.0</td>\n","      <td>79.0</td>\n","      <td>98.0</td>\n","      <td>110.0</td>\n","      <td>104.0</td>\n","      <td>72.0</td>\n","      <td>40.0</td>\n","      <td>26.0</td>\n","      <td>24.0</td>\n","      <td>33.0</td>\n","      <td>40.0</td>\n","      <td>29.0</td>\n","      <td>13.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>32.0</td>\n","      <td>52.0</td>\n","      <td>61.0</td>\n","      <td>54.0</td>\n","      <td>19.0</td>\n","      <td>...</td>\n","      <td>73.0</td>\n","      <td>55.0</td>\n","      <td>33.0</td>\n","      <td>15.0</td>\n","      <td>-5.0</td>\n","      <td>-18.0</td>\n","      <td>-24.0</td>\n","      <td>-28.0</td>\n","      <td>-15.0</td>\n","      <td>12.0</td>\n","      <td>33.0</td>\n","      <td>48.0</td>\n","      <td>47.0</td>\n","      <td>36.0</td>\n","      <td>19.0</td>\n","      <td>16.0</td>\n","      <td>31.0</td>\n","      <td>56.0</td>\n","      <td>69.0</td>\n","      <td>75.0</td>\n","      <td>67.0</td>\n","      <td>66.0</td>\n","      <td>76.0</td>\n","      <td>93.0</td>\n","      <td>88.0</td>\n","      <td>65.0</td>\n","      <td>56.0</td>\n","      <td>58.0</td>\n","      <td>63.0</td>\n","      <td>48.0</td>\n","      <td>29.0</td>\n","      <td>3.0</td>\n","      <td>-17.0</td>\n","      <td>-14.0</td>\n","      <td>-21.0</td>\n","      <td>-29.0</td>\n","      <td>-39.0</td>\n","      <td>-33.0</td>\n","      <td>-28.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>199</th>\n","      <td>-20.0</td>\n","      <td>-12.0</td>\n","      <td>-6.0</td>\n","      <td>6.0</td>\n","      <td>8.0</td>\n","      <td>-2.0</td>\n","      <td>-22.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-46.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-46.0</td>\n","      <td>-46.0</td>\n","      <td>-35.0</td>\n","      <td>-23.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-17.0</td>\n","      <td>-23.0</td>\n","      <td>-25.0</td>\n","      <td>-18.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-16.0</td>\n","      <td>-20.0</td>\n","      <td>-36.0</td>\n","      <td>-46.0</td>\n","      <td>-54.0</td>\n","      <td>-64.0</td>\n","      <td>-64.0</td>\n","      <td>-63.0</td>\n","      <td>-55.0</td>\n","      <td>-53.0</td>\n","      <td>-44.0</td>\n","      <td>-43.0</td>\n","      <td>-47.0</td>\n","      <td>-44.0</td>\n","      <td>-29.0</td>\n","      <td>...</td>\n","      <td>31.0</td>\n","      <td>26.0</td>\n","      <td>20.0</td>\n","      <td>21.0</td>\n","      <td>27.0</td>\n","      <td>33.0</td>\n","      <td>34.0</td>\n","      <td>42.0</td>\n","      <td>49.0</td>\n","      <td>37.0</td>\n","      <td>18.0</td>\n","      <td>-1.0</td>\n","      <td>-18.0</td>\n","      <td>-25.0</td>\n","      <td>-16.0</td>\n","      <td>6.0</td>\n","      <td>23.0</td>\n","      <td>37.0</td>\n","      <td>44.0</td>\n","      <td>39.0</td>\n","      <td>26.0</td>\n","      <td>14.0</td>\n","      <td>13.0</td>\n","      <td>11.0</td>\n","      <td>25.0</td>\n","      <td>38.0</td>\n","      <td>42.0</td>\n","      <td>31.0</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","      <td>1.0</td>\n","      <td>7.0</td>\n","      <td>20.0</td>\n","      <td>19.0</td>\n","      <td>11.0</td>\n","      <td>10.0</td>\n","      <td>12.0</td>\n","      <td>9.0</td>\n","      <td>2.0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>200 rows × 4097 columns</p>\n","</div>"],"text/plain":["         0      1      2       3      4  ...   4092   4093   4094   4095  tag\n","0    -18.0  -55.0 -126.0  -202.0 -238.0  ... -222.0 -224.0 -200.0 -127.0    1\n","1    -26.0    1.0   29.0    41.0   33.0  ... -205.0 -209.0 -207.0 -210.0    1\n","2     68.0 -106.0 -149.0  -141.0 -109.0  ...  554.0  460.0  343.0  247.0    1\n","3    343.0  311.0  284.0   274.0  260.0  ...  515.0  527.0  480.0  397.0    1\n","4    -63.0 -107.0 -208.0  -310.0 -395.0  ...  -32.0   56.0   44.0  -37.0    1\n","..     ...    ...    ...     ...    ...  ...    ...    ...    ...    ...  ...\n","195  -19.0  -39.0  -41.0   -16.0    5.0  ...   55.0   67.0   60.0   46.0    0\n","196 -459.0 -778.0 -981.0 -1048.0 -734.0  ...   -6.0   -3.0   -8.0  -11.0    1\n","197  213.0  210.0  210.0   212.0  194.0  ...   -8.0   -9.0   -2.0   20.0    1\n","198   24.0   10.0   -9.0   -18.0   -9.0  ...  -29.0  -39.0  -33.0  -28.0    0\n","199  -20.0  -12.0   -6.0     6.0    8.0  ...   10.0   12.0    9.0    2.0    0\n","\n","[200 rows x 4097 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NqxQFTvUz2iF","executionInfo":{"status":"ok","timestamp":1619875981517,"user_tz":-330,"elapsed":1203,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"ddcff460-dc5d-4965-c978-a4e494a2d628"},"source":["# Time Steps of LSTM\n","data_length = 4096\n","timesteps = 2048\n","data_dim = data_length//timesteps\n","data_dim"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"osm7OyT1z6YD","executionInfo":{"status":"ok","timestamp":1619875984079,"user_tz":-330,"elapsed":1519,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"146b3d1e-2e79-4a0e-aeab-16e9e5ea70e1"},"source":["#breaking dataset into X nd y\n","df1=df.values     #df1 is numpy.ndarray and df is pandas.dataframe\n","X, y = df1[:, :-1], df1[:, -1]\n","print(df.shape)\n","print(X.shape)\n","print(y.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(200, 4097)\n","(200, 4096)\n","(200,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h_yzzTtMz6Vi","executionInfo":{"status":"ok","timestamp":1619875988171,"user_tz":-330,"elapsed":1205,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"83929525-e27d-4288-8503-81221dee7f99"},"source":["#breaking X nd y into train nd test\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1, stratify=y)\n","\n","print(X_train.shape)\n","print(X_test.shape)\n","print(y_train.shape)\n","print(y_test.shape)\n","\n","X_train=X_train.reshape([X_train.shape[0], timesteps, data_dim])\n","X_test = X_test.reshape([X_test.shape[0], timesteps, data_dim])\n","y_train=np_utils.to_categorical(y_train, num_classes=2)\n","y_test=np_utils.to_categorical(y_test, num_classes=2)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(140, 4096)\n","(60, 4096)\n","(140,)\n","(60,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N8TZuFTNz6SZ","executionInfo":{"status":"ok","timestamp":1619875971237,"user_tz":-330,"elapsed":689,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"986d4d2f-de2b-4c62-8ebd-43fb070c5b05"},"source":["print(X_train.shape)\n","print(X_test.shape)\n","print(y_train.shape)\n","print(y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(140, 2048, 2)\n","(60, 2048, 2)\n","(140, 2)\n","(60, 2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mojDOduIz6Qc","executionInfo":{"status":"ok","timestamp":1619607903802,"user_tz":-330,"elapsed":96040,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"64ab93aa-72f2-4b3e-f375-124e367a53e1"},"source":["nb_epoch=100\n","\n","model = Sequential()\n","\n","model.add(Bidirectional(LSTM(80, input_shape= (timesteps, data_dim), return_sequences = True)))\n","model.add(Dropout(0.1))\n","\n","model.add(TimeDistributed(Dense(50)))\n","\n","model.add(GlobalAveragePooling1D())\n","\n","model.add(Dense(2, activation='softmax'))\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","#print(model.summary())\n","history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=64, epochs=nb_epoch)\n","\n","\n","plt.plot(history.history['specificity'], 'b--')\n","plt.plot(history.history['sensitivity'], 'g--')\n","plt.plot(history.history['accuracy'], 'r--')\n","plt.title('Model Different Metrics')\n","plt.ylabel('Metrics')\n","plt.xlabel('Epoch #')\n","plt.show()\n","\n","\n","\n","scores = model.evaluate(X_test, y_test, verbose=0)\n","print(\"Sensitivity = %.2f%%\" % (scores[1]*100))\n","print(\"Specificity = %.2f%%\" % (scores[2]*100))\n","print(\"Classification Accuracy = %.2f%%\" % (scores[3]*100))\n","\n","\n","%matplotlib inline\n","plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","3/3 [==============================] - 36s 656ms/step - loss: 0.6758 - sensitivity: 0.6934 - specificity: 0.6934 - accuracy: 0.6556 - val_loss: 0.5154 - val_sensitivity: 0.6833 - val_specificity: 0.6833 - val_accuracy: 0.6833\n","Epoch 2/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.4903 - sensitivity: 0.7845 - specificity: 0.7845 - accuracy: 0.7545 - val_loss: 0.3637 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 3/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.3394 - sensitivity: 0.9909 - specificity: 0.9909 - accuracy: 0.9890 - val_loss: 0.2666 - val_sensitivity: 0.9667 - val_specificity: 0.9667 - val_accuracy: 0.9667\n","Epoch 4/100\n","3/3 [==============================] - 0s 178ms/step - loss: 0.2559 - sensitivity: 0.9470 - specificity: 0.9470 - accuracy: 0.9516 - val_loss: 0.1897 - val_sensitivity: 0.9833 - val_specificity: 0.9833 - val_accuracy: 0.9833\n","Epoch 5/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.1942 - sensitivity: 0.9701 - specificity: 0.9701 - accuracy: 0.9662 - val_loss: 0.1331 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 6/100\n","3/3 [==============================] - 0s 174ms/step - loss: 0.1279 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0971 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 7/100\n","3/3 [==============================] - 0s 172ms/step - loss: 0.0970 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0738 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 8/100\n","3/3 [==============================] - 0s 175ms/step - loss: 0.0716 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0540 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 9/100\n","3/3 [==============================] - 0s 178ms/step - loss: 0.0534 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0395 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 10/100\n","3/3 [==============================] - 0s 175ms/step - loss: 0.0400 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0302 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 11/100\n","3/3 [==============================] - 0s 179ms/step - loss: 0.0331 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0245 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 12/100\n","3/3 [==============================] - 0s 174ms/step - loss: 0.0286 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0219 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 13/100\n","3/3 [==============================] - 0s 175ms/step - loss: 0.0260 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0194 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 14/100\n","3/3 [==============================] - 0s 173ms/step - loss: 0.0188 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0164 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 15/100\n","3/3 [==============================] - 0s 179ms/step - loss: 0.0169 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0158 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 16/100\n","3/3 [==============================] - 0s 174ms/step - loss: 0.0131 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0145 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 17/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.0129 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0153 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 18/100\n","3/3 [==============================] - 1s 179ms/step - loss: 0.0137 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0107 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 19/100\n","3/3 [==============================] - 0s 178ms/step - loss: 0.0092 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0083 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 20/100\n","3/3 [==============================] - 0s 173ms/step - loss: 0.0101 - sensitivity: 0.9954 - specificity: 0.9954 - accuracy: 0.9945 - val_loss: 0.0089 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 21/100\n","3/3 [==============================] - 0s 178ms/step - loss: 0.0081 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0078 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 22/100\n","3/3 [==============================] - 0s 173ms/step - loss: 0.0076 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0118 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 23/100\n","3/3 [==============================] - 0s 174ms/step - loss: 0.0088 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0124 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 24/100\n","3/3 [==============================] - 0s 175ms/step - loss: 0.0080 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0103 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 25/100\n","3/3 [==============================] - 0s 177ms/step - loss: 0.0054 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0096 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 26/100\n","3/3 [==============================] - 0s 175ms/step - loss: 0.0045 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0096 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 27/100\n","3/3 [==============================] - 0s 179ms/step - loss: 0.0052 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0083 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 28/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.0040 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0067 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 29/100\n","3/3 [==============================] - 0s 181ms/step - loss: 0.0034 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0054 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 30/100\n","3/3 [==============================] - 0s 178ms/step - loss: 0.0033 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0044 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 31/100\n","3/3 [==============================] - 0s 179ms/step - loss: 0.0029 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0037 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 32/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.0025 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0032 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 33/100\n","3/3 [==============================] - 1s 182ms/step - loss: 0.0020 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0029 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 34/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.0019 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0026 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 35/100\n","3/3 [==============================] - 0s 181ms/step - loss: 0.0016 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0025 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 36/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.0019 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0026 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 37/100\n","3/3 [==============================] - 0s 182ms/step - loss: 0.0015 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0026 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 38/100\n","3/3 [==============================] - 0s 179ms/step - loss: 0.0016 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0024 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 39/100\n","3/3 [==============================] - 1s 182ms/step - loss: 0.0013 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0021 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 40/100\n","3/3 [==============================] - 0s 176ms/step - loss: 0.0013 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0019 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 41/100\n","3/3 [==============================] - 0s 180ms/step - loss: 0.0010 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0017 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 42/100\n","3/3 [==============================] - 0s 177ms/step - loss: 0.0010 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0016 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 43/100\n","3/3 [==============================] - 0s 179ms/step - loss: 9.2087e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0015 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 44/100\n","3/3 [==============================] - 0s 180ms/step - loss: 8.7343e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0015 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 45/100\n","3/3 [==============================] - 0s 178ms/step - loss: 8.0164e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0014 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 46/100\n","3/3 [==============================] - 0s 181ms/step - loss: 8.2923e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0014 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 47/100\n","3/3 [==============================] - 0s 178ms/step - loss: 6.2825e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0014 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 48/100\n","3/3 [==============================] - 0s 177ms/step - loss: 6.3878e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0015 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 49/100\n","3/3 [==============================] - 0s 179ms/step - loss: 6.4553e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0014 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 50/100\n","3/3 [==============================] - 1s 188ms/step - loss: 6.4839e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0013 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 51/100\n","3/3 [==============================] - 1s 181ms/step - loss: 5.5217e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0012 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 52/100\n","3/3 [==============================] - 0s 178ms/step - loss: 4.1234e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0011 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 53/100\n","3/3 [==============================] - 0s 179ms/step - loss: 4.7865e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0011 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 54/100\n","3/3 [==============================] - 0s 179ms/step - loss: 4.0698e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0010 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 55/100\n","3/3 [==============================] - 0s 180ms/step - loss: 3.5853e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0010 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 56/100\n","3/3 [==============================] - 0s 177ms/step - loss: 3.8095e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 9.7546e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 57/100\n","3/3 [==============================] - 0s 179ms/step - loss: 3.3909e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 9.1231e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 58/100\n","3/3 [==============================] - 0s 181ms/step - loss: 3.0457e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 8.4916e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 59/100\n","3/3 [==============================] - 1s 179ms/step - loss: 2.9024e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 7.9202e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 60/100\n","3/3 [==============================] - 0s 181ms/step - loss: 3.1008e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 7.4302e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 61/100\n","3/3 [==============================] - 0s 180ms/step - loss: 2.9338e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 7.0751e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 62/100\n","3/3 [==============================] - 1s 185ms/step - loss: 2.1537e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.7044e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 63/100\n","3/3 [==============================] - 0s 178ms/step - loss: 2.4951e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.4518e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 64/100\n","3/3 [==============================] - 0s 175ms/step - loss: 2.3171e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.3435e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 65/100\n","3/3 [==============================] - 0s 179ms/step - loss: 2.3490e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.3283e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 66/100\n","3/3 [==============================] - 0s 182ms/step - loss: 2.5262e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.2731e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 67/100\n","3/3 [==============================] - 1s 180ms/step - loss: 2.2357e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.1588e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 68/100\n","3/3 [==============================] - 1s 182ms/step - loss: 2.2683e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.0405e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 69/100\n","3/3 [==============================] - 0s 181ms/step - loss: 1.8795e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.9013e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 70/100\n","3/3 [==============================] - 0s 178ms/step - loss: 1.7201e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.7963e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 71/100\n","3/3 [==============================] - 1s 183ms/step - loss: 1.4336e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.9126e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 72/100\n","3/3 [==============================] - 1s 182ms/step - loss: 1.6976e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.2641e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 73/100\n","3/3 [==============================] - 0s 181ms/step - loss: 1.3666e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.5805e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 74/100\n","3/3 [==============================] - 0s 179ms/step - loss: 1.5183e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.7322e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 75/100\n","3/3 [==============================] - 1s 182ms/step - loss: 1.4006e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.7352e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 76/100\n","3/3 [==============================] - 1s 181ms/step - loss: 1.4207e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.5748e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 77/100\n","3/3 [==============================] - 0s 181ms/step - loss: 1.3737e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 6.2967e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 78/100\n","3/3 [==============================] - 0s 181ms/step - loss: 1.4627e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.9684e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 79/100\n","3/3 [==============================] - 0s 180ms/step - loss: 1.2990e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.6362e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 80/100\n","3/3 [==============================] - 0s 179ms/step - loss: 1.0911e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.4188e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 81/100\n","3/3 [==============================] - 1s 184ms/step - loss: 1.1451e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.2526e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 82/100\n","3/3 [==============================] - 0s 182ms/step - loss: 1.1248e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 5.0860e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 83/100\n","3/3 [==============================] - 0s 181ms/step - loss: 1.1085e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.9476e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 84/100\n","3/3 [==============================] - 0s 182ms/step - loss: 1.0067e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.8138e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 85/100\n","3/3 [==============================] - 1s 185ms/step - loss: 1.0455e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.7597e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 86/100\n","3/3 [==============================] - 0s 181ms/step - loss: 1.1071e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.7008e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 87/100\n","3/3 [==============================] - 1s 182ms/step - loss: 9.4601e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.6084e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 88/100\n","3/3 [==============================] - 0s 178ms/step - loss: 1.0285e-04 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.4166e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 89/100\n","3/3 [==============================] - 0s 180ms/step - loss: 9.6114e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.2834e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 90/100\n","3/3 [==============================] - 0s 179ms/step - loss: 9.7186e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.1889e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 91/100\n","3/3 [==============================] - 1s 186ms/step - loss: 9.0692e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.1387e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 92/100\n","3/3 [==============================] - 1s 182ms/step - loss: 8.9767e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.0617e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 93/100\n","3/3 [==============================] - 1s 190ms/step - loss: 8.3619e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.0306e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 94/100\n","3/3 [==============================] - 1s 184ms/step - loss: 8.3250e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 3.9951e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 95/100\n","3/3 [==============================] - 1s 184ms/step - loss: 7.8475e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 3.9555e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 96/100\n","3/3 [==============================] - 0s 180ms/step - loss: 8.3900e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.0587e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 97/100\n","3/3 [==============================] - 1s 185ms/step - loss: 7.7013e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.1682e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 98/100\n","3/3 [==============================] - 0s 181ms/step - loss: 7.5314e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.3434e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 99/100\n","3/3 [==============================] - 1s 188ms/step - loss: 6.7410e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.4653e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n","Epoch 100/100\n","3/3 [==============================] - 1s 182ms/step - loss: 6.7832e-05 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 4.5756e-04 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_accuracy: 1.0000\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/javascript":["/* Put everything inside the global mpl namespace */\n","window.mpl = {};\n","\n","\n","mpl.get_websocket_type = function() {\n","    if (typeof(WebSocket) !== 'undefined') {\n","        return WebSocket;\n","    } else if (typeof(MozWebSocket) !== 'undefined') {\n","        return MozWebSocket;\n","    } else {\n","        alert('Your browser does not have WebSocket support. ' +\n","              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n","              'Firefox 4 and 5 are also supported but you ' +\n","              'have to enable WebSockets in about:config.');\n","    };\n","}\n","\n","mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n","    this.id = figure_id;\n","\n","    this.ws = websocket;\n","\n","    this.supports_binary = (this.ws.binaryType != undefined);\n","\n","    if (!this.supports_binary) {\n","        var warnings = document.getElementById(\"mpl-warnings\");\n","        if (warnings) {\n","            warnings.style.display = 'block';\n","            warnings.textContent = (\n","                \"This browser does not support binary websocket messages. \" +\n","                    \"Performance may be slow.\");\n","        }\n","    }\n","\n","    this.imageObj = new Image();\n","\n","    this.context = undefined;\n","    this.message = undefined;\n","    this.canvas = undefined;\n","    this.rubberband_canvas = undefined;\n","    this.rubberband_context = undefined;\n","    this.format_dropdown = undefined;\n","\n","    this.image_mode = 'full';\n","\n","    this.root = $('<div/>');\n","    this._root_extra_style(this.root)\n","    this.root.attr('style', 'display: inline-block');\n","\n","    $(parent_element).append(this.root);\n","\n","    this._init_header(this);\n","    this._init_canvas(this);\n","    this._init_toolbar(this);\n","\n","    var fig = this;\n","\n","    this.waiting = false;\n","\n","    this.ws.onopen =  function () {\n","            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n","            fig.send_message(\"send_image_mode\", {});\n","            if (mpl.ratio != 1) {\n","                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n","            }\n","            fig.send_message(\"refresh\", {});\n","        }\n","\n","    this.imageObj.onload = function() {\n","            if (fig.image_mode == 'full') {\n","                // Full images could contain transparency (where diff images\n","                // almost always do), so we need to clear the canvas so that\n","                // there is no ghosting.\n","                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n","            }\n","            fig.context.drawImage(fig.imageObj, 0, 0);\n","        };\n","\n","    this.imageObj.onunload = function() {\n","        fig.ws.close();\n","    }\n","\n","    this.ws.onmessage = this._make_on_message_function(this);\n","\n","    this.ondownload = ondownload;\n","}\n","\n","mpl.figure.prototype._init_header = function() {\n","    var titlebar = $(\n","        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n","        'ui-helper-clearfix\"/>');\n","    var titletext = $(\n","        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n","        'text-align: center; padding: 3px;\"/>');\n","    titlebar.append(titletext)\n","    this.root.append(titlebar);\n","    this.header = titletext[0];\n","}\n","\n","\n","\n","mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n","\n","}\n","\n","\n","mpl.figure.prototype._root_extra_style = function(canvas_div) {\n","\n","}\n","\n","mpl.figure.prototype._init_canvas = function() {\n","    var fig = this;\n","\n","    var canvas_div = $('<div/>');\n","\n","    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n","\n","    function canvas_keyboard_event(event) {\n","        return fig.key_event(event, event['data']);\n","    }\n","\n","    canvas_div.keydown('key_press', canvas_keyboard_event);\n","    canvas_div.keyup('key_release', canvas_keyboard_event);\n","    this.canvas_div = canvas_div\n","    this._canvas_extra_style(canvas_div)\n","    this.root.append(canvas_div);\n","\n","    var canvas = $('<canvas/>');\n","    canvas.addClass('mpl-canvas');\n","    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n","\n","    this.canvas = canvas[0];\n","    this.context = canvas[0].getContext(\"2d\");\n","\n","    var backingStore = this.context.backingStorePixelRatio ||\n","\tthis.context.webkitBackingStorePixelRatio ||\n","\tthis.context.mozBackingStorePixelRatio ||\n","\tthis.context.msBackingStorePixelRatio ||\n","\tthis.context.oBackingStorePixelRatio ||\n","\tthis.context.backingStorePixelRatio || 1;\n","\n","    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n","\n","    var rubberband = $('<canvas/>');\n","    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n","\n","    var pass_mouse_events = true;\n","\n","    canvas_div.resizable({\n","        start: function(event, ui) {\n","            pass_mouse_events = false;\n","        },\n","        resize: function(event, ui) {\n","            fig.request_resize(ui.size.width, ui.size.height);\n","        },\n","        stop: function(event, ui) {\n","            pass_mouse_events = true;\n","            fig.request_resize(ui.size.width, ui.size.height);\n","        },\n","    });\n","\n","    function mouse_event_fn(event) {\n","        if (pass_mouse_events)\n","            return fig.mouse_event(event, event['data']);\n","    }\n","\n","    rubberband.mousedown('button_press', mouse_event_fn);\n","    rubberband.mouseup('button_release', mouse_event_fn);\n","    // Throttle sequential mouse events to 1 every 20ms.\n","    rubberband.mousemove('motion_notify', mouse_event_fn);\n","\n","    rubberband.mouseenter('figure_enter', mouse_event_fn);\n","    rubberband.mouseleave('figure_leave', mouse_event_fn);\n","\n","    canvas_div.on(\"wheel\", function (event) {\n","        event = event.originalEvent;\n","        event['data'] = 'scroll'\n","        if (event.deltaY < 0) {\n","            event.step = 1;\n","        } else {\n","            event.step = -1;\n","        }\n","        mouse_event_fn(event);\n","    });\n","\n","    canvas_div.append(canvas);\n","    canvas_div.append(rubberband);\n","\n","    this.rubberband = rubberband;\n","    this.rubberband_canvas = rubberband[0];\n","    this.rubberband_context = rubberband[0].getContext(\"2d\");\n","    this.rubberband_context.strokeStyle = \"#000000\";\n","\n","    this._resize_canvas = function(width, height) {\n","        // Keep the size of the canvas, canvas container, and rubber band\n","        // canvas in synch.\n","        canvas_div.css('width', width)\n","        canvas_div.css('height', height)\n","\n","        canvas.attr('width', width * mpl.ratio);\n","        canvas.attr('height', height * mpl.ratio);\n","        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n","\n","        rubberband.attr('width', width);\n","        rubberband.attr('height', height);\n","    }\n","\n","    // Set the figure to an initial 600x600px, this will subsequently be updated\n","    // upon first draw.\n","    this._resize_canvas(600, 600);\n","\n","    // Disable right mouse context menu.\n","    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n","        return false;\n","    });\n","\n","    function set_focus () {\n","        canvas.focus();\n","        canvas_div.focus();\n","    }\n","\n","    window.setTimeout(set_focus, 100);\n","}\n","\n","mpl.figure.prototype._init_toolbar = function() {\n","    var fig = this;\n","\n","    var nav_element = $('<div/>');\n","    nav_element.attr('style', 'width: 100%');\n","    this.root.append(nav_element);\n","\n","    // Define a callback function for later on.\n","    function toolbar_event(event) {\n","        return fig.toolbar_button_onclick(event['data']);\n","    }\n","    function toolbar_mouse_event(event) {\n","        return fig.toolbar_button_onmouseover(event['data']);\n","    }\n","\n","    for(var toolbar_ind in mpl.toolbar_items) {\n","        var name = mpl.toolbar_items[toolbar_ind][0];\n","        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n","        var image = mpl.toolbar_items[toolbar_ind][2];\n","        var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n","        if (!name) {\n","            // put a spacer in here.\n","            continue;\n","        }\n","        var button = $('<button/>');\n","        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n","                        'ui-button-icon-only');\n","        button.attr('role', 'button');\n","        button.attr('aria-disabled', 'false');\n","        button.click(method_name, toolbar_event);\n","        button.mouseover(tooltip, toolbar_mouse_event);\n","\n","        var icon_img = $('<span/>');\n","        icon_img.addClass('ui-button-icon-primary ui-icon');\n","        icon_img.addClass(image);\n","        icon_img.addClass('ui-corner-all');\n","\n","        var tooltip_span = $('<span/>');\n","        tooltip_span.addClass('ui-button-text');\n","        tooltip_span.html(tooltip);\n","\n","        button.append(icon_img);\n","        button.append(tooltip_span);\n","\n","        nav_element.append(button);\n","    }\n","\n","    var fmt_picker_span = $('<span/>');\n","\n","    var fmt_picker = $('<select/>');\n","    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n","    fmt_picker_span.append(fmt_picker);\n","    nav_element.append(fmt_picker_span);\n","    this.format_dropdown = fmt_picker[0];\n","\n","    for (var ind in mpl.extensions) {\n","        var fmt = mpl.extensions[ind];\n","        var option = $(\n","            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n","        fmt_picker.append(option);\n","    }\n","\n","    // Add hover states to the ui-buttons\n","    $( \".ui-button\" ).hover(\n","        function() { $(this).addClass(\"ui-state-hover\");},\n","        function() { $(this).removeClass(\"ui-state-hover\");}\n","    );\n","\n","    var status_bar = $('<span class=\"mpl-message\"/>');\n","    nav_element.append(status_bar);\n","    this.message = status_bar[0];\n","}\n","\n","mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n","    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n","    // which will in turn request a refresh of the image.\n","    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n","}\n","\n","mpl.figure.prototype.send_message = function(type, properties) {\n","    properties['type'] = type;\n","    properties['figure_id'] = this.id;\n","    this.ws.send(JSON.stringify(properties));\n","}\n","\n","mpl.figure.prototype.send_draw_message = function() {\n","    if (!this.waiting) {\n","        this.waiting = true;\n","        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n","    }\n","}\n","\n","\n","mpl.figure.prototype.handle_save = function(fig, msg) {\n","    var format_dropdown = fig.format_dropdown;\n","    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n","    fig.ondownload(fig, format);\n","}\n","\n","\n","mpl.figure.prototype.handle_resize = function(fig, msg) {\n","    var size = msg['size'];\n","    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n","        fig._resize_canvas(size[0], size[1]);\n","        fig.send_message(\"refresh\", {});\n","    };\n","}\n","\n","mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n","    var x0 = msg['x0'] / mpl.ratio;\n","    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n","    var x1 = msg['x1'] / mpl.ratio;\n","    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n","    x0 = Math.floor(x0) + 0.5;\n","    y0 = Math.floor(y0) + 0.5;\n","    x1 = Math.floor(x1) + 0.5;\n","    y1 = Math.floor(y1) + 0.5;\n","    var min_x = Math.min(x0, x1);\n","    var min_y = Math.min(y0, y1);\n","    var width = Math.abs(x1 - x0);\n","    var height = Math.abs(y1 - y0);\n","\n","    fig.rubberband_context.clearRect(\n","        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n","\n","    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n","}\n","\n","mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n","    // Updates the figure title.\n","    fig.header.textContent = msg['label'];\n","}\n","\n","mpl.figure.prototype.handle_cursor = function(fig, msg) {\n","    var cursor = msg['cursor'];\n","    switch(cursor)\n","    {\n","    case 0:\n","        cursor = 'pointer';\n","        break;\n","    case 1:\n","        cursor = 'default';\n","        break;\n","    case 2:\n","        cursor = 'crosshair';\n","        break;\n","    case 3:\n","        cursor = 'move';\n","        break;\n","    }\n","    fig.rubberband_canvas.style.cursor = cursor;\n","}\n","\n","mpl.figure.prototype.handle_message = function(fig, msg) {\n","    fig.message.textContent = msg['message'];\n","}\n","\n","mpl.figure.prototype.handle_draw = function(fig, msg) {\n","    // Request the server to send over a new figure.\n","    fig.send_draw_message();\n","}\n","\n","mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n","    fig.image_mode = msg['mode'];\n","}\n","\n","mpl.figure.prototype.updated_canvas_event = function() {\n","    // Called whenever the canvas gets updated.\n","    this.send_message(\"ack\", {});\n","}\n","\n","// A function to construct a web socket function for onmessage handling.\n","// Called in the figure constructor.\n","mpl.figure.prototype._make_on_message_function = function(fig) {\n","    return function socket_on_message(evt) {\n","        if (evt.data instanceof Blob) {\n","            /* FIXME: We get \"Resource interpreted as Image but\n","             * transferred with MIME type text/plain:\" errors on\n","             * Chrome.  But how to set the MIME type?  It doesn't seem\n","             * to be part of the websocket stream */\n","            evt.data.type = \"image/png\";\n","\n","            /* Free the memory for the previous frames */\n","            if (fig.imageObj.src) {\n","                (window.URL || window.webkitURL).revokeObjectURL(\n","                    fig.imageObj.src);\n","            }\n","\n","            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n","                evt.data);\n","            fig.updated_canvas_event();\n","            fig.waiting = false;\n","            return;\n","        }\n","        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n","            fig.imageObj.src = evt.data;\n","            fig.updated_canvas_event();\n","            fig.waiting = false;\n","            return;\n","        }\n","\n","        var msg = JSON.parse(evt.data);\n","        var msg_type = msg['type'];\n","\n","        // Call the  \"handle_{type}\" callback, which takes\n","        // the figure and JSON message as its only arguments.\n","        try {\n","            var callback = fig[\"handle_\" + msg_type];\n","        } catch (e) {\n","            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n","            return;\n","        }\n","\n","        if (callback) {\n","            try {\n","                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n","                callback(fig, msg);\n","            } catch (e) {\n","                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n","            }\n","        }\n","    };\n","}\n","\n","// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n","mpl.findpos = function(e) {\n","    //this section is from http://www.quirksmode.org/js/events_properties.html\n","    var targ;\n","    if (!e)\n","        e = window.event;\n","    if (e.target)\n","        targ = e.target;\n","    else if (e.srcElement)\n","        targ = e.srcElement;\n","    if (targ.nodeType == 3) // defeat Safari bug\n","        targ = targ.parentNode;\n","\n","    // jQuery normalizes the pageX and pageY\n","    // pageX,Y are the mouse positions relative to the document\n","    // offset() returns the position of the element relative to the document\n","    var x = e.pageX - $(targ).offset().left;\n","    var y = e.pageY - $(targ).offset().top;\n","\n","    return {\"x\": x, \"y\": y};\n","};\n","\n","/*\n"," * return a copy of an object with only non-object keys\n"," * we need this to avoid circular references\n"," * http://stackoverflow.com/a/24161582/3208463\n"," */\n","function simpleKeys (original) {\n","  return Object.keys(original).reduce(function (obj, key) {\n","    if (typeof original[key] !== 'object')\n","        obj[key] = original[key]\n","    return obj;\n","  }, {});\n","}\n","\n","mpl.figure.prototype.mouse_event = function(event, name) {\n","    var canvas_pos = mpl.findpos(event)\n","\n","    if (name === 'button_press')\n","    {\n","        this.canvas.focus();\n","        this.canvas_div.focus();\n","    }\n","\n","    var x = canvas_pos.x * mpl.ratio;\n","    var y = canvas_pos.y * mpl.ratio;\n","\n","    this.send_message(name, {x: x, y: y, button: event.button,\n","                             step: event.step,\n","                             guiEvent: simpleKeys(event)});\n","\n","    /* This prevents the web browser from automatically changing to\n","     * the text insertion cursor when the button is pressed.  We want\n","     * to control all of the cursor setting manually through the\n","     * 'cursor' event from matplotlib */\n","    event.preventDefault();\n","    return false;\n","}\n","\n","mpl.figure.prototype._key_event_extra = function(event, name) {\n","    // Handle any extra behaviour associated with a key event\n","}\n","\n","mpl.figure.prototype.key_event = function(event, name) {\n","\n","    // Prevent repeat events\n","    if (name == 'key_press')\n","    {\n","        if (event.which === this._key)\n","            return;\n","        else\n","            this._key = event.which;\n","    }\n","    if (name == 'key_release')\n","        this._key = null;\n","\n","    var value = '';\n","    if (event.ctrlKey && event.which != 17)\n","        value += \"ctrl+\";\n","    if (event.altKey && event.which != 18)\n","        value += \"alt+\";\n","    if (event.shiftKey && event.which != 16)\n","        value += \"shift+\";\n","\n","    value += 'k';\n","    value += event.which.toString();\n","\n","    this._key_event_extra(event, name);\n","\n","    this.send_message(name, {key: value,\n","                             guiEvent: simpleKeys(event)});\n","    return false;\n","}\n","\n","mpl.figure.prototype.toolbar_button_onclick = function(name) {\n","    if (name == 'download') {\n","        this.handle_save(this, null);\n","    } else {\n","        this.send_message(\"toolbar_button\", {name: name});\n","    }\n","};\n","\n","mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n","    this.message.textContent = tooltip;\n","};\n","mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n","\n","mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n","\n","mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n","    // Create a \"websocket\"-like object which calls the given IPython comm\n","    // object with the appropriate methods. Currently this is a non binary\n","    // socket, so there is still some room for performance tuning.\n","    var ws = {};\n","\n","    ws.close = function() {\n","        comm.close()\n","    };\n","    ws.send = function(m) {\n","        //console.log('sending', m);\n","        comm.send(m);\n","    };\n","    // Register the callback with on_msg.\n","    comm.on_msg(function(msg) {\n","        //console.log('receiving', msg['content']['data'], msg);\n","        // Pass the mpl event to the overridden (by mpl) onmessage function.\n","        ws.onmessage(msg['content']['data'])\n","    });\n","    return ws;\n","}\n","\n","mpl.mpl_figure_comm = function(comm, msg) {\n","    // This is the function which gets called when the mpl process\n","    // starts-up an IPython Comm through the \"matplotlib\" channel.\n","\n","    var id = msg.content.data.id;\n","    // Get hold of the div created by the display call when the Comm\n","    // socket was opened in Python.\n","    var element = $(\"#\" + id);\n","    var ws_proxy = comm_websocket_adapter(comm)\n","\n","    function ondownload(figure, format) {\n","        window.open(figure.imageObj.src);\n","    }\n","\n","    var fig = new mpl.figure(id, ws_proxy,\n","                           ondownload,\n","                           element.get(0));\n","\n","    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n","    // web socket which is closed, not our websocket->open comm proxy.\n","    ws_proxy.onopen();\n","\n","    fig.parent_element = element.get(0);\n","    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n","    if (!fig.cell_info) {\n","        console.error(\"Failed to find cell for figure\", id, fig);\n","        return;\n","    }\n","\n","    var output_index = fig.cell_info[2]\n","    var cell = fig.cell_info[0];\n","\n","};\n","\n","mpl.figure.prototype.handle_close = function(fig, msg) {\n","    var width = fig.canvas.width/mpl.ratio\n","    fig.root.unbind('remove')\n","\n","    // Update the output cell to use the data from the current canvas.\n","    fig.push_to_output();\n","    var dataURL = fig.canvas.toDataURL();\n","    // Re-enable the keyboard manager in IPython - without this line, in FF,\n","    // the notebook keyboard shortcuts fail.\n","    IPython.keyboard_manager.enable()\n","    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n","    fig.close_ws(fig, msg);\n","}\n","\n","mpl.figure.prototype.close_ws = function(fig, msg){\n","    fig.send_message('closing', msg);\n","    // fig.ws.close()\n","}\n","\n","mpl.figure.prototype.push_to_output = function(remove_interactive) {\n","    // Turn the data on the canvas into data in the output cell.\n","    var width = this.canvas.width/mpl.ratio\n","    var dataURL = this.canvas.toDataURL();\n","    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n","}\n","\n","mpl.figure.prototype.updated_canvas_event = function() {\n","    // Tell IPython that the notebook contents must change.\n","    IPython.notebook.set_dirty(true);\n","    this.send_message(\"ack\", {});\n","    var fig = this;\n","    // Wait a second, then push the new image to the DOM so\n","    // that it is saved nicely (might be nice to debounce this).\n","    setTimeout(function () { fig.push_to_output() }, 1000);\n","}\n","\n","mpl.figure.prototype._init_toolbar = function() {\n","    var fig = this;\n","\n","    var nav_element = $('<div/>');\n","    nav_element.attr('style', 'width: 100%');\n","    this.root.append(nav_element);\n","\n","    // Define a callback function for later on.\n","    function toolbar_event(event) {\n","        return fig.toolbar_button_onclick(event['data']);\n","    }\n","    function toolbar_mouse_event(event) {\n","        return fig.toolbar_button_onmouseover(event['data']);\n","    }\n","\n","    for(var toolbar_ind in mpl.toolbar_items){\n","        var name = mpl.toolbar_items[toolbar_ind][0];\n","        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n","        var image = mpl.toolbar_items[toolbar_ind][2];\n","        var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n","        if (!name) { continue; };\n","\n","        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n","        button.click(method_name, toolbar_event);\n","        button.mouseover(tooltip, toolbar_mouse_event);\n","        nav_element.append(button);\n","    }\n","\n","    // Add the status bar.\n","    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n","    nav_element.append(status_bar);\n","    this.message = status_bar[0];\n","\n","    // Add the close button to the window.\n","    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n","    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n","    button.click(function (evt) { fig.handle_close(fig, {}); } );\n","    button.mouseover('Stop Interaction', toolbar_mouse_event);\n","    buttongrp.append(button);\n","    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n","    titlebar.prepend(buttongrp);\n","}\n","\n","mpl.figure.prototype._root_extra_style = function(el){\n","    var fig = this\n","    el.on(\"remove\", function(){\n","\tfig.close_ws(fig, {});\n","    });\n","}\n","\n","mpl.figure.prototype._canvas_extra_style = function(el){\n","    // this is important to make the div 'focusable\n","    el.attr('tabindex', 0)\n","    // reach out to IPython and tell the keyboard manager to turn it's self\n","    // off when our div gets focus\n","\n","    // location in version 3\n","    if (IPython.notebook.keyboard_manager) {\n","        IPython.notebook.keyboard_manager.register_events(el);\n","    }\n","    else {\n","        // location in version 2\n","        IPython.keyboard_manager.register_events(el);\n","    }\n","\n","}\n","\n","mpl.figure.prototype._key_event_extra = function(event, name) {\n","    var manager = IPython.notebook.keyboard_manager;\n","    if (!manager)\n","        manager = IPython.keyboard_manager;\n","\n","    // Check for shift+enter\n","    if (event.shiftKey && event.which == 13) {\n","        this.canvas_div.blur();\n","        // select the cell after this one\n","        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n","        IPython.notebook.select(index + 1);\n","    }\n","}\n","\n","mpl.figure.prototype.handle_save = function(fig, msg) {\n","    fig.ondownload(fig, null);\n","}\n","\n","\n","mpl.find_output_cell = function(html_output) {\n","    // Return the cell and output element which can be found *uniquely* in the notebook.\n","    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n","    // IPython event is triggered only after the cells have been serialised, which for\n","    // our purposes (turning an active figure into a static one), is too late.\n","    var cells = IPython.notebook.get_cells();\n","    var ncells = cells.length;\n","    for (var i=0; i<ncells; i++) {\n","        var cell = cells[i];\n","        if (cell.cell_type === 'code'){\n","            for (var j=0; j<cell.output_area.outputs.length; j++) {\n","                var data = cell.output_area.outputs[j];\n","                if (data.data) {\n","                    // IPython >= 3 moved mimebundle to data attribute of output\n","                    data = data.data;\n","                }\n","                if (data['text/html'] == html_output) {\n","                    return [cell, data, j];\n","                }\n","            }\n","        }\n","    }\n","}\n","\n","// Register the function which deals with the matplotlib target/channel.\n","// The kernel may be null if the page has been refreshed.\n","if (IPython.notebook.kernel != null) {\n","    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n","}\n"],"text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["<div id='0d65fde4-1930-4b9b-af4a-86c8a533bad5'></div>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Sensitivity = 100.00%\n","Specificity = 100.00%\n","Classification Accuracy = 100.00%\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h5Eu18u0q5KT","executionInfo":{"status":"ok","timestamp":1619607904790,"user_tz":-330,"elapsed":97015,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"b62b0809-081d-49f4-ad36-ff2659e3cd76"},"source":["#check output of model\n","y_predict=model.predict(X_test)\n","\n","# numpy implementation of argmax\n","from numpy import argmax\n","from numpy import asarray\n","# define vector\n","probs = asarray(y_predict)\n","print(probs.shape)\n","# get argmax\n","result = argmax(probs, axis=1)\n","print(result)\n","\n","y_test_cm=argmax(y_test,axis=1)\n","print(result.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(60, 2)\n","[0 1 0 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 1\n"," 0 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 0 0 1 1 0 0]\n","(60,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uPEQNWPjuJoW","executionInfo":{"status":"ok","timestamp":1619607904792,"user_tz":-330,"elapsed":95348,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"2214c1bd-f131-4e1a-f6bc-2a71ea89708f"},"source":["\n","from sklearn.metrics import confusion_matrix\n","confusion = confusion_matrix(y_test_cm, result)\n","print('Confusion Matrix\\n')\n","print(confusion)\n","\n","#importing accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","print('\\nAccuracy: {:.2f}\\n'.format(accuracy_score(y_test_cm, result)))\n","\n","print('Micro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='micro')))\n","print('Micro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='micro')))\n","print('Micro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='micro')))\n","\n","print('Macro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='macro')))\n","print('Macro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='macro')))\n","print('Macro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='macro')))\n","\n","print('Weighted Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='weighted')))\n","print('Weighted Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='weighted')))\n","print('Weighted F1-score: {:.2f}'.format(f1_score(y_test_cm, result, average='weighted')))\n","\n","from sklearn.metrics import classification_report\n","print('\\nClassification Report\\n')\n","print(classification_report(y_test_cm, result, target_names=['Class A', 'Class E']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Confusion Matrix\n","\n","[[30  0]\n"," [ 0 30]]\n","\n","Accuracy: 1.00\n","\n","Micro Precision: 1.00\n","Micro Recall: 1.00\n","Micro F1-score: 1.00\n","\n","Macro Precision: 1.00\n","Macro Recall: 1.00\n","Macro F1-score: 1.00\n","\n","Weighted Precision: 1.00\n","Weighted Recall: 1.00\n","Weighted F1-score: 1.00\n","\n","Classification Report\n","\n","              precision    recall  f1-score   support\n","\n","     Class A       1.00      1.00      1.00        30\n","     Class E       1.00      1.00      1.00        30\n","\n","    accuracy                           1.00        60\n","   macro avg       1.00      1.00      1.00        60\n","weighted avg       1.00      1.00      1.00        60\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wkHVOSrqJk8B"},"source":["# Saving model nd its weight"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Sq_kK36JiDb","executionInfo":{"status":"ok","timestamp":1619607945584,"user_tz":-330,"elapsed":1009,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"8d18e1b5-c3ae-4387-a247-a1fc0074fe1a"},"source":["# serialize model to JSON\n","model_json = model.to_json()\n","with open(\"/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_AvsE.json\", \"w\") as json_file:\n","    json_file.write(model_json)\n","# serialize weights to HDF5\n","model.save_weights(\"/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_AvsE.h5\")\n","print(\"Saved model to disk\")\n"," "],"execution_count":null,"outputs":[{"output_type":"stream","text":["Saved model to disk\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"US4h94cK1ryN"},"source":["# (B.1) 2 class classification [ABCD vs E]"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"id":"wBAfn8nYz6OA","executionInfo":{"status":"ok","timestamp":1619868317748,"user_tz":-330,"elapsed":2598,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"fb76a271-ad0c-4c50-d405-320e1dbaac3a"},"source":["df=pd.read_csv(\"/content/drive/My Drive/mtech_finalyr_project/modified_Dataset/final_csv_with_shuffle_ABvsCDvsE.csv\")\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","  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<td>-82.0</td>\n","      <td>-57.0</td>\n","      <td>-18.0</td>\n","      <td>-3.0</td>\n","      <td>-11.0</td>\n","      <td>-1.0</td>\n","      <td>41.0</td>\n","      <td>120.0</td>\n","      <td>181.0</td>\n","      <td>202.0</td>\n","      <td>198.0</td>\n","      <td>195.0</td>\n","      <td>197.0</td>\n","      <td>190.0</td>\n","      <td>...</td>\n","      <td>-110.0</td>\n","      <td>-108.0</td>\n","      <td>-112.0</td>\n","      <td>-113.0</td>\n","      <td>-112.0</td>\n","      <td>-103.0</td>\n","      <td>-100.0</td>\n","      <td>-98.0</td>\n","      <td>-95.0</td>\n","      <td>-94.0</td>\n","      <td>-92.0</td>\n","      <td>-91.0</td>\n","      <td>-89.0</td>\n","      <td>-86.0</td>\n","      <td>-84.0</td>\n","      <td>-78.0</td>\n","      <td>-70.0</td>\n","      <td>-63.0</td>\n","      <td>-53.0</td>\n","      <td>-46.0</td>\n","      <td>-37.0</td>\n","      <td>-34.0</td>\n","      <td>-29.0</td>\n","      <td>-28.0</td>\n","      <td>-23.0</td>\n","      <td>-15.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-9.0</td>\n","      <td>-10.0</td>\n","      <td>-5.0</td>\n","      <td>-2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>-2.0</td>\n","      <td>-8.0</td>\n","      <td>-9.0</td>\n","      <td>-2.0</td>\n","      <td>20.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>496</th>\n","      <td>24.0</td>\n","      <td>10.0</td>\n","      <td>-9.0</td>\n","      <td>-18.0</td>\n","      <td>-9.0</td>\n","      <td>-3.0</td>\n","      <td>-2.0</td>\n","      <td>-2.0</td>\n","      <td>-9.0</td>\n","      <td>-9.0</td>\n","      <td>-14.0</td>\n","      <td>-14.0</td>\n","      <td>-24.0</td>\n","      <td>-24.0</td>\n","      <td>-12.0</td>\n","      <td>-1.0</td>\n","      <td>13.0</td>\n","      <td>31.0</td>\n","      <td>60.0</td>\n","      <td>79.0</td>\n","      <td>98.0</td>\n","      <td>110.0</td>\n","      <td>104.0</td>\n","      <td>72.0</td>\n","      <td>40.0</td>\n","      <td>26.0</td>\n","      <td>24.0</td>\n","      <td>33.0</td>\n","      <td>40.0</td>\n","      <td>29.0</td>\n","      <td>13.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>32.0</td>\n","      <td>52.0</td>\n","      <td>61.0</td>\n","      <td>54.0</td>\n","      <td>19.0</td>\n","      <td>...</td>\n","      <td>73.0</td>\n","      <td>55.0</td>\n","      <td>33.0</td>\n","      <td>15.0</td>\n","      <td>-5.0</td>\n","      <td>-18.0</td>\n","      <td>-24.0</td>\n","      <td>-28.0</td>\n","      <td>-15.0</td>\n","      <td>12.0</td>\n","      <td>33.0</td>\n","      <td>48.0</td>\n","      <td>47.0</td>\n","      <td>36.0</td>\n","      <td>19.0</td>\n","      <td>16.0</td>\n","      <td>31.0</td>\n","      <td>56.0</td>\n","      <td>69.0</td>\n","      <td>75.0</td>\n","      <td>67.0</td>\n","      <td>66.0</td>\n","      <td>76.0</td>\n","      <td>93.0</td>\n","      <td>88.0</td>\n","      <td>65.0</td>\n","      <td>56.0</td>\n","      <td>58.0</td>\n","      <td>63.0</td>\n","      <td>48.0</td>\n","      <td>29.0</td>\n","      <td>3.0</td>\n","      <td>-17.0</td>\n","      <td>-14.0</td>\n","      <td>-21.0</td>\n","      <td>-29.0</td>\n","      <td>-39.0</td>\n","      <td>-33.0</td>\n","      <td>-28.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>-20.0</td>\n","      <td>-12.0</td>\n","      <td>-6.0</td>\n","      <td>6.0</td>\n","      <td>8.0</td>\n","      <td>-2.0</td>\n","      <td>-22.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-46.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-46.0</td>\n","      <td>-46.0</td>\n","      <td>-35.0</td>\n","      <td>-23.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-17.0</td>\n","      <td>-23.0</td>\n","      <td>-25.0</td>\n","      <td>-18.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-16.0</td>\n","      <td>-20.0</td>\n","      <td>-36.0</td>\n","      <td>-46.0</td>\n","      <td>-54.0</td>\n","      <td>-64.0</td>\n","      <td>-64.0</td>\n","      <td>-63.0</td>\n","      <td>-55.0</td>\n","      <td>-53.0</td>\n","      <td>-44.0</td>\n","      <td>-43.0</td>\n","      <td>-47.0</td>\n","      <td>-44.0</td>\n","      <td>-29.0</td>\n","      <td>...</td>\n","      <td>31.0</td>\n","      <td>26.0</td>\n","      <td>20.0</td>\n","      <td>21.0</td>\n","      <td>27.0</td>\n","      <td>33.0</td>\n","      <td>34.0</td>\n","      <td>42.0</td>\n","      <td>49.0</td>\n","      <td>37.0</td>\n","      <td>18.0</td>\n","      <td>-1.0</td>\n","      <td>-18.0</td>\n","      <td>-25.0</td>\n","      <td>-16.0</td>\n","      <td>6.0</td>\n","      <td>23.0</td>\n","      <td>37.0</td>\n","      <td>44.0</td>\n","      <td>39.0</td>\n","      <td>26.0</td>\n","      <td>14.0</td>\n","      <td>13.0</td>\n","      <td>11.0</td>\n","      <td>25.0</td>\n","      <td>38.0</td>\n","      <td>42.0</td>\n","      <td>31.0</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","      <td>1.0</td>\n","      <td>7.0</td>\n","      <td>20.0</td>\n","      <td>19.0</td>\n","      <td>11.0</td>\n","      <td>10.0</td>\n","      <td>12.0</td>\n","      <td>9.0</td>\n","      <td>2.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>-104.0</td>\n","      <td>-100.0</td>\n","      <td>-94.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-88.0</td>\n","      <td>-90.0</td>\n","      <td>-83.0</td>\n","      <td>-75.0</td>\n","      <td>-69.0</td>\n","      <td>-59.0</td>\n","      <td>-50.0</td>\n","      <td>-38.0</td>\n","      <td>-42.0</td>\n","      <td>-50.0</td>\n","      <td>-67.0</td>\n","      <td>-84.0</td>\n","      <td>-92.0</td>\n","      <td>-96.0</td>\n","      <td>-97.0</td>\n","      <td>-99.0</td>\n","      <td>-100.0</td>\n","      <td>-96.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-107.0</td>\n","      <td>-126.0</td>\n","      <td>-131.0</td>\n","      <td>-136.0</td>\n","      <td>-128.0</td>\n","      <td>-121.0</td>\n","      <td>-107.0</td>\n","      <td>-95.0</td>\n","      <td>-87.0</td>\n","      <td>-82.0</td>\n","      <td>-82.0</td>\n","      <td>-86.0</td>\n","      <td>-90.0</td>\n","      <td>...</td>\n","      <td>-28.0</td>\n","      <td>-22.0</td>\n","      <td>-23.0</td>\n","      <td>-19.0</td>\n","      <td>-20.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-23.0</td>\n","      <td>-37.0</td>\n","      <td>-56.0</td>\n","      <td>-64.0</td>\n","      <td>-67.0</td>\n","      <td>-64.0</td>\n","      <td>-49.0</td>\n","      <td>-40.0</td>\n","      <td>-34.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-35.0</td>\n","      <td>-33.0</td>\n","      <td>-31.0</td>\n","      <td>-24.0</td>\n","      <td>-11.0</td>\n","      <td>1.0</td>\n","      <td>8.0</td>\n","      <td>6.0</td>\n","      <td>2.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-27.0</td>\n","      <td>-38.0</td>\n","      <td>-45.0</td>\n","      <td>-40.0</td>\n","      <td>-30.0</td>\n","      <td>-37.0</td>\n","      <td>-45.0</td>\n","      <td>-59.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>-43.0</td>\n","      <td>-57.0</td>\n","      <td>-86.0</td>\n","      <td>-106.0</td>\n","      <td>-106.0</td>\n","      <td>-120.0</td>\n","      <td>-111.0</td>\n","      <td>-88.0</td>\n","      <td>-81.0</td>\n","      <td>-90.0</td>\n","      <td>-109.0</td>\n","      <td>-102.0</td>\n","      <td>-69.0</td>\n","      <td>-20.0</td>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>42.0</td>\n","      <td>68.0</td>\n","      <td>61.0</td>\n","      <td>36.0</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>9.0</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>-2.0</td>\n","      <td>-13.0</td>\n","      <td>-23.0</td>\n","      <td>-30.0</td>\n","      <td>-39.0</td>\n","      <td>-13.0</td>\n","      <td>29.0</td>\n","      <td>48.0</td>\n","      <td>63.0</td>\n","      <td>67.0</td>\n","      <td>90.0</td>\n","      <td>96.0</td>\n","      <td>84.0</td>\n","      <td>81.0</td>\n","      <td>76.0</td>\n","      <td>...</td>\n","      <td>-129.0</td>\n","      <td>-117.0</td>\n","      <td>-130.0</td>\n","      <td>-132.0</td>\n","      <td>-134.0</td>\n","      <td>-150.0</td>\n","      <td>-161.0</td>\n","      <td>-191.0</td>\n","      <td>-180.0</td>\n","      <td>-145.0</td>\n","      <td>-135.0</td>\n","      <td>-123.0</td>\n","      <td>-139.0</td>\n","      <td>-127.0</td>\n","      <td>-103.0</td>\n","      <td>-111.0</td>\n","      <td>-95.0</td>\n","      <td>-82.0</td>\n","      <td>-55.0</td>\n","      <td>-19.0</td>\n","      <td>-11.0</td>\n","      <td>4.0</td>\n","      <td>16.0</td>\n","      <td>-22.0</td>\n","      <td>-1.0</td>\n","      <td>-20.0</td>\n","      <td>-11.0</td>\n","      <td>6.0</td>\n","      <td>17.0</td>\n","      <td>22.0</td>\n","      <td>3.0</td>\n","      <td>-9.0</td>\n","      <td>6.0</td>\n","      <td>39.0</td>\n","      <td>59.0</td>\n","      <td>80.0</td>\n","      <td>39.0</td>\n","      <td>21.0</td>\n","      <td>-8.0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>500 rows × 4097 columns</p>\n","</div>"],"text/plain":["         0      1      2      3      4  ...   4092   4093   4094   4095  tag\n","0    -18.0  -55.0 -126.0 -202.0 -238.0  ... -222.0 -224.0 -200.0 -127.0    1\n","1    -26.0    1.0   29.0   41.0   33.0  ... -205.0 -209.0 -207.0 -210.0    1\n","2     68.0 -106.0 -149.0 -141.0 -109.0  ...  554.0  460.0  343.0  247.0    1\n","3    343.0  311.0  284.0  274.0  260.0  ...  515.0  527.0  480.0  397.0    1\n","4    -63.0 -107.0 -208.0 -310.0 -395.0  ...  -32.0   56.0   44.0  -37.0    1\n","..     ...    ...    ...    ...    ...  ...    ...    ...    ...    ...  ...\n","495  213.0  210.0  210.0  212.0  194.0  ...   -8.0   -9.0   -2.0   20.0    1\n","496   24.0   10.0   -9.0  -18.0   -9.0  ...  -29.0  -39.0  -33.0  -28.0    0\n","497  -20.0  -12.0   -6.0    6.0    8.0  ...   10.0   12.0    9.0    2.0    0\n","498 -104.0 -100.0  -94.0  -87.0  -87.0  ...  -30.0  -37.0  -45.0  -59.0    0\n","499  -43.0  -57.0  -86.0 -106.0 -106.0  ...   80.0   39.0   21.0   -8.0    0\n","\n","[500 rows x 4097 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a60X9rzMz6D3","executionInfo":{"status":"ok","timestamp":1619868325182,"user_tz":-330,"elapsed":1055,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"4cb90061-e919-4212-f4bb-8e52f3bb7959"},"source":["#breaking dataset into X nd y\n","df1=df.values     #df1 is numpy.ndarray and df is pandas.dataframe\n","X, y = df1[:, :-1], df1[:, -1]\n","print(df.shape)\n","print(X.shape)\n","print(y.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(500, 4097)\n","(500, 4096)\n","(500,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dqfxGLOw1NPM","executionInfo":{"status":"ok","timestamp":1619870940543,"user_tz":-330,"elapsed":1274,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"12c1a97f-1892-4c9b-b2c3-2de798e1ef72"},"source":["type(X_test)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hjNuP7FP27OH","executionInfo":{"status":"ok","timestamp":1619868331779,"user_tz":-330,"elapsed":1239,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"2efbd309-71f7-41ea-c6dc-8d6b82700de4"},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1, stratify=y)\n","\n","print(X_train.shape)\n","print(X_test.shape)\n","print(y_train.shape)\n","print(y_test.shape)\n","\n","X_train=X_train.reshape([X_train.shape[0], timesteps, data_dim])\n","X_test = X_test.reshape([X_test.shape[0], timesteps, data_dim])\n","y_train=np_utils.to_categorical(y_train, num_classes=2)\n","y_test=np_utils.to_categorical(y_test, num_classes=2)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(350, 4096)\n","(150, 4096)\n","(350,)\n","(150,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gm-4ZeaB2-hZ","executionInfo":{"status":"ok","timestamp":1619607975470,"user_tz":-330,"elapsed":2549,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"7d3f3182-95c1-4291-beee-f581f78cc6e9"},"source":["print(X_train.shape)\n","print(X_test.shape)\n","print(y_train.shape)\n","print(y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(350, 2048, 2)\n","(150, 2048, 2)\n","(350, 2)\n","(150, 2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"340qyyS73BMS"},"source":["nb_epoch=100\n","\n","model = Sequential()\n","\n","model.add(Bidirectional(LSTM(80, input_shape= (timesteps, data_dim), return_sequences = True)))\n","model.add(Dropout(0.1))\n","\n","model.add(TimeDistributed(Dense(50)))\n","\n","model.add(GlobalAveragePooling1D())\n","\n","model.add(Dense(2, activation='softmax'))\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","#print(model.summary())\n","history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=64, epochs=nb_epoch)\n","\n","\n","plt.plot(history.history['specificity'], 'b--')\n","plt.plot(history.history['sensitivity'], 'g--')\n","plt.plot(history.history['accuracy'], 'r--')\n","plt.title('Model Different Metrics')\n","plt.ylabel('Metrics')\n","plt.xlabel('Epoch #')\n","plt.show()\n","\n","\n","\n","scores = model.evaluate(X_test, y_test, verbose=0)\n","print(\"Sensitivity = %.2f%%\" % (scores[1]*100))\n","print(\"Specificity = %.2f%%\" % (scores[2]*100))\n","print(\"Classification Accuracy = %.2f%%\" % (scores[3]*100))\n","\n","\n","%matplotlib inline\n","plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LSCCkNrA_Isq","executionInfo":{"status":"ok","timestamp":1619608083676,"user_tz":-330,"elapsed":110731,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"53acd4f7-510a-4ba2-8dec-7b3cb9a3f972"},"source":["scores"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.004568497184664011, 1.0, 1.0, 1.0]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ron8g-EAzuq5","executionInfo":{"status":"ok","timestamp":1619608084655,"user_tz":-330,"elapsed":111699,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"dcc39547-a4d2-4bcb-a6e0-c75e114f61f0"},"source":["#check output of model\n","y_predict=model.predict(X_test)\n","\n","# numpy implementation of argmax\n","from numpy import argmax\n","from numpy import asarray\n","# define vector\n","probs = asarray(y_predict)\n","print(probs.shape)\n","# get argmax\n","result = argmax(probs, axis=1)\n","print(result)\n","\n","y_test_cm=argmax(y_test,axis=1)\n","print(result.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(150, 2)\n","[0 0 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n"," 1 1 0 0 0 0 1 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0\n"," 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0\n"," 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n"," 0 0]\n","(150,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XLQG9-sR3EFO","executionInfo":{"status":"ok","timestamp":1619608084659,"user_tz":-330,"elapsed":111667,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"a7a819b2-a361-44c0-e7f6-a2870704abbd"},"source":["confusion = confusion_matrix(y_test_cm, result)\n","print('Confusion Matrix\\n')\n","print(confusion)\n","\n","#importing accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","print('\\nAccuracy: {:.2f}\\n'.format(accuracy_score(y_test_cm, result)))\n","\n","print('Micro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='micro')))\n","print('Micro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='micro')))\n","print('Micro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='micro')))\n","\n","print('Macro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='macro')))\n","print('Macro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='macro')))\n","print('Macro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='macro')))\n","\n","print('Weighted Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='weighted')))\n","print('Weighted Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='weighted')))\n","print('Weighted F1-score: {:.2f}'.format(f1_score(y_test_cm, result, average='weighted')))\n","\n","from sklearn.metrics import classification_report\n","print('\\nClassification Report\\n')\n","print(classification_report(y_test_cm, result, target_names=['Class ABCD', 'Class E']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Confusion Matrix\n","\n","[[120   0]\n"," [  0  30]]\n","\n","Accuracy: 1.00\n","\n","Micro Precision: 1.00\n","Micro Recall: 1.00\n","Micro F1-score: 1.00\n","\n","Macro Precision: 1.00\n","Macro Recall: 1.00\n","Macro F1-score: 1.00\n","\n","Weighted Precision: 1.00\n","Weighted Recall: 1.00\n","Weighted F1-score: 1.00\n","\n","Classification Report\n","\n","              precision    recall  f1-score   support\n","\n","  Class ABCD       1.00      1.00      1.00       120\n","     Class E       1.00      1.00      1.00        30\n","\n","    accuracy                           1.00       150\n","   macro avg       1.00      1.00      1.00       150\n","weighted avg       1.00      1.00      1.00       150\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"EeGlhL2HHjCn"},"source":["# serialize model to JSON\n","model_json = model.to_json()\n","with open(\"/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.json\", \"w\") as json_file:\n","    json_file.write(model_json)\n","# serialize weights to HDF5\n","model.save_weights(\"/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.h5\")\n","print(\"Saved model to disk\")\n"," "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hQxoIc5prhMu","executionInfo":{"status":"ok","timestamp":1619869520392,"user_tz":-330,"elapsed":1334,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"db274734-0eb8-4634-b57f-e383692176f2"},"source":["#dataset of size 150*(4096/2)\n","print(\"shape of X_test: \",X_test.shape)\n","df_2048=X_test[:,0:2048].copy()\n","print(\"shape of df_2048: \",df_2048.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["shape of X_test:  (150, 4096)\n","shape of df_2048:  (150, 2048)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QmxzPE_cvbav","executionInfo":{"status":"ok","timestamp":1619869469684,"user_tz":-330,"elapsed":1270,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"6728a89b-9c53-4e17-9828-341d2454b812"},"source":["df_2048"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-148., -155., -156., ...,   30.,   38.,   41.],\n","       [  45.,   52.,   79., ...,  -48.,  -64.,  -71.],\n","       [ -20.,   -3.,    2., ...,   24.,   21.,    5.],\n","       ...,\n","       [  40.,   60.,   74., ..., -138., -135., -128.],\n","       [ -91., -116.,  -70., ...,  -12.,  -16.,  -25.],\n","       [ -41.,  -40.,  -39., ...,  -35.,  -70.,  -90.]])"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"id":"lxekX7iJzsT8","executionInfo":{"status":"ok","timestamp":1619870706903,"user_tz":-330,"elapsed":2090,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"0bef0825-83c5-4f63-e573-06ae60c63cdb"},"source":["X_test_2048 = pd.concat([pd.DataFrame(df_2048), pd.DataFrame(df_2048)], axis=1,  ignore_index=True)\n","X_test_2048"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","      <th>25</th>\n","      <th>26</th>\n","      <th>27</th>\n","      <th>28</th>\n","      <th>29</th>\n","      <th>30</th>\n","      <th>31</th>\n","      <th>32</th>\n","      <th>33</th>\n","      <th>34</th>\n","      <th>35</th>\n","      <th>36</th>\n","      <th>37</th>\n","      <th>38</th>\n","      <th>39</th>\n","      <th>...</th>\n","      <th>4056</th>\n","      <th>4057</th>\n","      <th>4058</th>\n","      <th>4059</th>\n","      <th>4060</th>\n","      <th>4061</th>\n","      <th>4062</th>\n","      <th>4063</th>\n","      <th>4064</th>\n","      <th>4065</th>\n","      <th>4066</th>\n","      <th>4067</th>\n","      <th>4068</th>\n","      <th>4069</th>\n","      <th>4070</th>\n","      <th>4071</th>\n","      <th>4072</th>\n","      <th>4073</th>\n","      <th>4074</th>\n","      <th>4075</th>\n","      <th>4076</th>\n","      <th>4077</th>\n","      <th>4078</th>\n","      <th>4079</th>\n","      <th>4080</th>\n","      <th>4081</th>\n","      <th>4082</th>\n","      <th>4083</th>\n","      <th>4084</th>\n","      <th>4085</th>\n","      <th>4086</th>\n","      <th>4087</th>\n","      <th>4088</th>\n","      <th>4089</th>\n","      <th>4090</th>\n","      <th>4091</th>\n","      <th>4092</th>\n","      <th>4093</th>\n","      <th>4094</th>\n","      <th>4095</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-148.0</td>\n","      <td>-155.0</td>\n","      <td>-156.0</td>\n","      <td>-156.0</td>\n","      <td>-150.0</td>\n","      <td>-147.0</td>\n","      <td>-141.0</td>\n","      <td>-135.0</td>\n","      <td>-131.0</td>\n","      <td>-124.0</td>\n","      <td>-114.0</td>\n","      <td>-100.0</td>\n","      <td>-80.0</td>\n","      <td>-51.0</td>\n","      <td>-21.0</td>\n","      <td>2.0</td>\n","      <td>22.0</td>\n","      <td>37.0</td>\n","      <td>49.0</td>\n","      <td>53.0</td>\n","      <td>50.0</td>\n","      <td>46.0</td>\n","      <td>38.0</td>\n","      <td>27.0</td>\n","      <td>21.0</td>\n","      <td>19.0</td>\n","      <td>18.0</td>\n","      <td>21.0</td>\n","      <td>22.0</td>\n","      <td>22.0</td>\n","      <td>24.0</td>\n","      <td>13.0</td>\n","      <td>-1.0</td>\n","      <td>-16.0</td>\n","      <td>-30.0</td>\n","      <td>-32.0</td>\n","      <td>-46.0</td>\n","      <td>-51.0</td>\n","      <td>-56.0</td>\n","      <td>-53.0</td>\n","      <td>...</td>\n","      <td>-43.0</td>\n","      <td>-35.0</td>\n","      <td>-28.0</td>\n","      <td>-16.0</td>\n","      <td>-10.0</td>\n","      <td>0.0</td>\n","      <td>13.0</td>\n","      <td>22.0</td>\n","      <td>29.0</td>\n","      <td>31.0</td>\n","      <td>31.0</td>\n","      <td>35.0</td>\n","      <td>36.0</td>\n","      <td>43.0</td>\n","      <td>48.0</td>\n","      <td>41.0</td>\n","      <td>29.0</td>\n","      <td>20.0</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-22.0</td>\n","      <td>-18.0</td>\n","      <td>-15.0</td>\n","      <td>-18.0</td>\n","      <td>-21.0</td>\n","      <td>-14.0</td>\n","      <td>-7.0</td>\n","      <td>-2.0</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","      <td>7.0</td>\n","      <td>10.0</td>\n","      <td>15.0</td>\n","      <td>19.0</td>\n","      <td>21.0</td>\n","      <td>24.0</td>\n","      <td>30.0</td>\n","      <td>38.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>45.0</td>\n","      <td>52.0</td>\n","      <td>79.0</td>\n","      <td>117.0</td>\n","      <td>146.0</td>\n","      <td>160.0</td>\n","      <td>178.0</td>\n","      <td>210.0</td>\n","      <td>235.0</td>\n","      <td>250.0</td>\n","      <td>248.0</td>\n","      <td>240.0</td>\n","      <td>229.0</td>\n","      <td>228.0</td>\n","      <td>209.0</td>\n","      <td>194.0</td>\n","      <td>170.0</td>\n","      <td>154.0</td>\n","      <td>131.0</td>\n","      <td>122.0</td>\n","      <td>103.0</td>\n","      <td>91.0</td>\n","      <td>79.0</td>\n","      <td>77.0</td>\n","      <td>86.0</td>\n","      <td>84.0</td>\n","      <td>70.0</td>\n","      <td>55.0</td>\n","      <td>21.0</td>\n","      <td>2.0</td>\n","      <td>-4.0</td>\n","      <td>-8.0</td>\n","      <td>-12.0</td>\n","      <td>-32.0</td>\n","      <td>-47.0</td>\n","      <td>-60.0</td>\n","      <td>-63.0</td>\n","      <td>-71.0</td>\n","      <td>-77.0</td>\n","      <td>-94.0</td>\n","      <td>...</td>\n","      <td>68.0</td>\n","      <td>89.0</td>\n","      <td>83.0</td>\n","      <td>102.0</td>\n","      <td>106.0</td>\n","      <td>117.0</td>\n","      <td>123.0</td>\n","      <td>109.0</td>\n","      <td>89.0</td>\n","      <td>33.0</td>\n","      <td>-2.0</td>\n","      <td>-21.0</td>\n","      <td>-26.0</td>\n","      <td>-14.0</td>\n","      <td>0.0</td>\n","      <td>19.0</td>\n","      <td>52.0</td>\n","      <td>46.0</td>\n","      <td>10.0</td>\n","      <td>-28.0</td>\n","      <td>-39.0</td>\n","      <td>-22.0</td>\n","      <td>2.0</td>\n","      <td>28.0</td>\n","      <td>31.0</td>\n","      <td>20.0</td>\n","      <td>15.0</td>\n","      <td>6.0</td>\n","      <td>6.0</td>\n","      <td>-12.0</td>\n","      <td>-8.0</td>\n","      <td>-3.0</td>\n","      <td>-10.0</td>\n","      <td>-36.0</td>\n","      <td>-57.0</td>\n","      <td>-73.0</td>\n","      <td>-56.0</td>\n","      <td>-55.0</td>\n","      <td>-48.0</td>\n","      <td>-64.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-20.0</td>\n","      <td>-3.0</td>\n","      <td>2.0</td>\n","      <td>16.0</td>\n","      <td>29.0</td>\n","      <td>36.0</td>\n","      <td>34.0</td>\n","      <td>29.0</td>\n","      <td>38.0</td>\n","      <td>26.0</td>\n","      <td>6.0</td>\n","      <td>-1.0</td>\n","      <td>-4.0</td>\n","      <td>13.0</td>\n","      <td>24.0</td>\n","      <td>38.0</td>\n","      <td>27.0</td>\n","      <td>21.0</td>\n","      <td>15.0</td>\n","      <td>12.0</td>\n","      <td>5.0</td>\n","      <td>7.0</td>\n","      <td>3.0</td>\n","      <td>-7.0</td>\n","      <td>-15.0</td>\n","      <td>-18.0</td>\n","      <td>-13.0</td>\n","      <td>-19.0</td>\n","      <td>-14.0</td>\n","      <td>-17.0</td>\n","      <td>-15.0</td>\n","      <td>-4.0</td>\n","      <td>-5.0</td>\n","      <td>-11.0</td>\n","      <td>-4.0</td>\n","      <td>10.0</td>\n","      <td>23.0</td>\n","      <td>20.0</td>\n","      <td>5.0</td>\n","      <td>-6.0</td>\n","      <td>...</td>\n","      <td>-68.0</td>\n","      <td>-72.0</td>\n","      <td>-64.0</td>\n","      <td>-50.0</td>\n","      <td>-34.0</td>\n","      <td>-22.0</td>\n","      <td>-19.0</td>\n","      <td>-13.0</td>\n","      <td>-25.0</td>\n","      <td>-48.0</td>\n","      <td>-52.0</td>\n","      <td>-53.0</td>\n","      <td>-41.0</td>\n","      <td>-30.0</td>\n","      <td>-16.0</td>\n","      <td>-5.0</td>\n","      <td>-9.0</td>\n","      <td>-6.0</td>\n","      <td>-16.0</td>\n","      <td>-27.0</td>\n","      <td>-35.0</td>\n","      <td>-32.0</td>\n","      <td>-32.0</td>\n","      <td>-26.0</td>\n","      <td>-12.0</td>\n","      <td>-19.0</td>\n","      <td>-38.0</td>\n","      <td>-56.0</td>\n","      <td>-54.0</td>\n","      <td>-55.0</td>\n","      <td>-46.0</td>\n","      <td>-48.0</td>\n","      <td>-42.0</td>\n","      <td>-43.0</td>\n","      <td>-26.0</td>\n","      <td>-8.0</td>\n","      <td>6.0</td>\n","      <td>22.0</td>\n","      <td>24.0</td>\n","      <td>21.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-28.0</td>\n","      <td>-20.0</td>\n","      <td>-18.0</td>\n","      <td>-22.0</td>\n","      <td>-23.0</td>\n","      <td>-23.0</td>\n","      <td>-15.0</td>\n","      <td>-8.0</td>\n","      <td>-7.0</td>\n","      <td>-3.0</td>\n","      <td>8.0</td>\n","      <td>19.0</td>\n","      <td>26.0</td>\n","      <td>40.0</td>\n","      <td>45.0</td>\n","      <td>52.0</td>\n","      <td>49.0</td>\n","      <td>52.0</td>\n","      <td>49.0</td>\n","      <td>48.0</td>\n","      <td>50.0</td>\n","      <td>42.0</td>\n","      <td>37.0</td>\n","      <td>29.0</td>\n","      <td>31.0</td>\n","      <td>26.0</td>\n","      <td>28.0</td>\n","      <td>30.0</td>\n","      <td>27.0</td>\n","      <td>23.0</td>\n","      <td>11.0</td>\n","      <td>9.0</td>\n","      <td>-2.0</td>\n","      <td>-2.0</td>\n","      <td>6.0</td>\n","      <td>7.0</td>\n","      <td>-4.0</td>\n","      <td>-5.0</td>\n","      <td>-5.0</td>\n","      <td>-1.0</td>\n","      <td>...</td>\n","      <td>-21.0</td>\n","      <td>-22.0</td>\n","      <td>-25.0</td>\n","      <td>-25.0</td>\n","      <td>-24.0</td>\n","      <td>-17.0</td>\n","      <td>-10.0</td>\n","      <td>-5.0</td>\n","      <td>-11.0</td>\n","      <td>-4.0</td>\n","      <td>9.0</td>\n","      <td>12.0</td>\n","      <td>15.0</td>\n","      <td>21.0</td>\n","      <td>25.0</td>\n","      <td>24.0</td>\n","      <td>28.0</td>\n","      <td>31.0</td>\n","      <td>37.0</td>\n","      <td>40.0</td>\n","      <td>38.0</td>\n","      <td>35.0</td>\n","      <td>26.0</td>\n","      <td>19.0</td>\n","      <td>9.0</td>\n","      <td>1.0</td>\n","      <td>-5.0</td>\n","      <td>6.0</td>\n","      <td>12.0</td>\n","      <td>10.0</td>\n","      <td>14.0</td>\n","      <td>-2.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-16.0</td>\n","      <td>-12.0</td>\n","      <td>-16.0</td>\n","      <td>-6.0</td>\n","      <td>-7.0</td>\n","      <td>-13.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>23.0</td>\n","      <td>144.0</td>\n","      <td>228.0</td>\n","      <td>260.0</td>\n","      <td>255.0</td>\n","      <td>218.0</td>\n","      <td>178.0</td>\n","      <td>126.0</td>\n","      <td>60.0</td>\n","      <td>6.0</td>\n","      <td>-47.0</td>\n","      <td>-90.0</td>\n","      <td>-143.0</td>\n","      <td>-181.0</td>\n","      <td>-221.0</td>\n","      <td>-264.0</td>\n","      <td>-305.0</td>\n","      <td>-346.0</td>\n","      <td>-406.0</td>\n","      <td>-506.0</td>\n","      <td>-565.0</td>\n","      <td>-474.0</td>\n","      <td>-232.0</td>\n","      <td>76.0</td>\n","      <td>309.0</td>\n","      <td>415.0</td>\n","      <td>484.0</td>\n","      <td>552.0</td>\n","      <td>594.0</td>\n","      <td>614.0</td>\n","      <td>578.0</td>\n","      <td>494.0</td>\n","      <td>400.0</td>\n","      <td>293.0</td>\n","      <td>192.0</td>\n","      <td>104.0</td>\n","      <td>26.0</td>\n","      <td>-22.0</td>\n","      <td>-32.0</td>\n","      <td>-46.0</td>\n","      <td>...</td>\n","      <td>-146.0</td>\n","      <td>-202.0</td>\n","      <td>-291.0</td>\n","      <td>-344.0</td>\n","      <td>-286.0</td>\n","      <td>-126.0</td>\n","      <td>82.0</td>\n","      <td>237.0</td>\n","      <td>326.0</td>\n","      <td>344.0</td>\n","      <td>313.0</td>\n","      <td>278.0</td>\n","      <td>262.0</td>\n","      <td>264.0</td>\n","      <td>282.0</td>\n","      <td>289.0</td>\n","      <td>276.0</td>\n","      <td>273.0</td>\n","      <td>238.0</td>\n","      <td>187.0</td>\n","      <td>110.0</td>\n","      <td>17.0</td>\n","      <td>-51.0</td>\n","      <td>-94.0</td>\n","      <td>-133.0</td>\n","      <td>-161.0</td>\n","      <td>-168.0</td>\n","      <td>-179.0</td>\n","      <td>-181.0</td>\n","      <td>-203.0</td>\n","      <td>-231.0</td>\n","      <td>-275.0</td>\n","      <td>-367.0</td>\n","      <td>-357.0</td>\n","      <td>-297.0</td>\n","      <td>-139.0</td>\n","      <td>28.0</td>\n","      <td>140.0</td>\n","      <td>240.0</td>\n","      <td>294.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>-55.0</td>\n","      <td>-48.0</td>\n","      <td>-48.0</td>\n","      <td>-38.0</td>\n","      <td>-23.0</td>\n","      <td>0.0</td>\n","      <td>11.0</td>\n","      <td>22.0</td>\n","      <td>21.0</td>\n","      <td>3.0</td>\n","      <td>-10.0</td>\n","      <td>-16.0</td>\n","      <td>-17.0</td>\n","      <td>-18.0</td>\n","      <td>-25.0</td>\n","      <td>-43.0</td>\n","      <td>-71.0</td>\n","      <td>-87.0</td>\n","      <td>-84.0</td>\n","      <td>-67.0</td>\n","      <td>-53.0</td>\n","      <td>-36.0</td>\n","      <td>-22.0</td>\n","      <td>-20.0</td>\n","      <td>-13.0</td>\n","      <td>-3.0</td>\n","      <td>-1.0</td>\n","      <td>-12.0</td>\n","      <td>-29.0</td>\n","      <td>-50.0</td>\n","      <td>-55.0</td>\n","      <td>-37.0</td>\n","      <td>-5.0</td>\n","      <td>17.0</td>\n","      <td>9.0</td>\n","      <td>-9.0</td>\n","      <td>-35.0</td>\n","      <td>-41.0</td>\n","      <td>-32.0</td>\n","      <td>-4.0</td>\n","      <td>...</td>\n","      <td>42.0</td>\n","      <td>54.0</td>\n","      <td>61.0</td>\n","      <td>62.0</td>\n","      <td>60.0</td>\n","      <td>39.0</td>\n","      <td>23.0</td>\n","      <td>20.0</td>\n","      <td>23.0</td>\n","      <td>27.0</td>\n","      <td>21.0</td>\n","      <td>7.0</td>\n","      <td>-18.0</td>\n","      <td>-33.0</td>\n","      <td>-29.0</td>\n","      <td>-27.0</td>\n","      <td>-23.0</td>\n","      <td>-20.0</td>\n","      <td>-7.0</td>\n","      <td>6.0</td>\n","      <td>22.0</td>\n","      <td>35.0</td>\n","      <td>43.0</td>\n","      <td>33.0</td>\n","      <td>11.0</td>\n","      <td>3.0</td>\n","      <td>-7.0</td>\n","      <td>5.0</td>\n","      <td>19.0</td>\n","      <td>26.0</td>\n","      <td>17.0</td>\n","      <td>12.0</td>\n","      <td>13.0</td>\n","      <td>16.0</td>\n","      <td>31.0</td>\n","      <td>54.0</td>\n","      <td>81.0</td>\n","      <td>106.0</td>\n","      <td>130.0</td>\n","      <td>142.0</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>-39.0</td>\n","      <td>-27.0</td>\n","      <td>-16.0</td>\n","      <td>-3.0</td>\n","      <td>17.0</td>\n","      <td>34.0</td>\n","      <td>73.0</td>\n","      <td>105.0</td>\n","      <td>142.0</td>\n","      <td>155.0</td>\n","      <td>156.0</td>\n","      <td>149.0</td>\n","      <td>135.0</td>\n","      <td>114.0</td>\n","      <td>91.0</td>\n","      <td>60.0</td>\n","      <td>53.0</td>\n","      <td>71.0</td>\n","      <td>105.0</td>\n","      <td>133.0</td>\n","      <td>138.0</td>\n","      <td>120.0</td>\n","      <td>110.0</td>\n","      <td>98.0</td>\n","      <td>97.0</td>\n","      <td>90.0</td>\n","      <td>85.0</td>\n","      <td>72.0</td>\n","      <td>70.0</td>\n","      <td>55.0</td>\n","      <td>42.0</td>\n","      <td>2.0</td>\n","      <td>-41.0</td>\n","      <td>-90.0</td>\n","      <td>-122.0</td>\n","      <td>-142.0</td>\n","      <td>-167.0</td>\n","      <td>-195.0</td>\n","      <td>-223.0</td>\n","      <td>-227.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>6.0</td>\n","      <td>-5.0</td>\n","      <td>-9.0</td>\n","      <td>-1.0</td>\n","      <td>5.0</td>\n","      <td>24.0</td>\n","      <td>43.0</td>\n","      <td>72.0</td>\n","      <td>94.0</td>\n","      <td>106.0</td>\n","      <td>107.0</td>\n","      <td>125.0</td>\n","      <td>144.0</td>\n","      <td>165.0</td>\n","      <td>173.0</td>\n","      <td>174.0</td>\n","      <td>181.0</td>\n","      <td>198.0</td>\n","      <td>205.0</td>\n","      <td>220.0</td>\n","      <td>220.0</td>\n","      <td>224.0</td>\n","      <td>209.0</td>\n","      <td>188.0</td>\n","      <td>163.0</td>\n","      <td>136.0</td>\n","      <td>118.0</td>\n","      <td>104.0</td>\n","      <td>91.0</td>\n","      <td>60.0</td>\n","      <td>41.0</td>\n","      <td>17.0</td>\n","      <td>2.0</td>\n","      <td>-3.0</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>-5.0</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>40.0</td>\n","      <td>60.0</td>\n","      <td>74.0</td>\n","      <td>70.0</td>\n","      <td>67.0</td>\n","      <td>48.0</td>\n","      <td>44.0</td>\n","      <td>44.0</td>\n","      <td>52.0</td>\n","      <td>69.0</td>\n","      <td>73.0</td>\n","      <td>66.0</td>\n","      <td>70.0</td>\n","      <td>78.0</td>\n","      <td>71.0</td>\n","      <td>68.0</td>\n","      <td>57.0</td>\n","      <td>55.0</td>\n","      <td>35.0</td>\n","      <td>24.0</td>\n","      <td>10.0</td>\n","      <td>-13.0</td>\n","      <td>-24.0</td>\n","      <td>-24.0</td>\n","      <td>-27.0</td>\n","      <td>-28.0</td>\n","      <td>-21.0</td>\n","      <td>-13.0</td>\n","      <td>6.0</td>\n","      <td>31.0</td>\n","      <td>37.0</td>\n","      <td>27.0</td>\n","      <td>-1.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>-13.0</td>\n","      <td>-30.0</td>\n","      <td>-21.0</td>\n","      <td>-6.0</td>\n","      <td>...</td>\n","      <td>-4.0</td>\n","      <td>-23.0</td>\n","      <td>-51.0</td>\n","      <td>-62.0</td>\n","      <td>-62.0</td>\n","      <td>-79.0</td>\n","      <td>-89.0</td>\n","      <td>-98.0</td>\n","      <td>-115.0</td>\n","      <td>-118.0</td>\n","      <td>-138.0</td>\n","      <td>-145.0</td>\n","      <td>-148.0</td>\n","      <td>-136.0</td>\n","      <td>-103.0</td>\n","      <td>-79.0</td>\n","      <td>-70.0</td>\n","      <td>-55.0</td>\n","      <td>-22.0</td>\n","      <td>17.0</td>\n","      <td>56.0</td>\n","      <td>79.0</td>\n","      <td>81.0</td>\n","      <td>69.0</td>\n","      <td>40.0</td>\n","      <td>1.0</td>\n","      <td>-33.0</td>\n","      <td>-50.0</td>\n","      <td>-45.0</td>\n","      <td>-26.0</td>\n","      <td>-33.0</td>\n","      <td>-52.0</td>\n","      <td>-74.0</td>\n","      <td>-99.0</td>\n","      <td>-99.0</td>\n","      <td>-109.0</td>\n","      <td>-119.0</td>\n","      <td>-146.0</td>\n","      <td>-138.0</td>\n","      <td>-135.0</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>-91.0</td>\n","      <td>-116.0</td>\n","      <td>-70.0</td>\n","      <td>5.0</td>\n","      <td>45.0</td>\n","      <td>56.0</td>\n","      <td>12.0</td>\n","      <td>-32.0</td>\n","      <td>-66.0</td>\n","      <td>-45.0</td>\n","      <td>-2.0</td>\n","      <td>23.0</td>\n","      <td>10.0</td>\n","      <td>-10.0</td>\n","      <td>-27.0</td>\n","      <td>-18.0</td>\n","      <td>20.0</td>\n","      <td>64.0</td>\n","      <td>81.0</td>\n","      <td>69.0</td>\n","      <td>21.0</td>\n","      <td>-18.0</td>\n","      <td>-43.0</td>\n","      <td>-26.0</td>\n","      <td>10.0</td>\n","      <td>48.0</td>\n","      <td>61.0</td>\n","      <td>49.0</td>\n","      <td>-2.0</td>\n","      <td>-34.0</td>\n","      <td>-50.0</td>\n","      <td>-47.0</td>\n","      <td>-48.0</td>\n","      <td>-58.0</td>\n","      <td>-54.0</td>\n","      <td>-52.0</td>\n","      <td>-28.0</td>\n","      <td>13.0</td>\n","      <td>28.0</td>\n","      <td>36.0</td>\n","      <td>...</td>\n","      <td>-79.0</td>\n","      <td>-51.0</td>\n","      <td>-29.0</td>\n","      <td>-3.0</td>\n","      <td>-12.0</td>\n","      <td>-9.0</td>\n","      <td>-22.0</td>\n","      <td>-34.0</td>\n","      <td>-49.0</td>\n","      <td>-36.0</td>\n","      <td>-30.0</td>\n","      <td>-19.0</td>\n","      <td>8.0</td>\n","      <td>21.0</td>\n","      <td>16.0</td>\n","      <td>-1.0</td>\n","      <td>-20.0</td>\n","      <td>-32.0</td>\n","      <td>-30.0</td>\n","      <td>-25.0</td>\n","      <td>-32.0</td>\n","      <td>-45.0</td>\n","      <td>-65.0</td>\n","      <td>-74.0</td>\n","      <td>-85.0</td>\n","      <td>-89.0</td>\n","      <td>-65.0</td>\n","      <td>-59.0</td>\n","      <td>-54.0</td>\n","      <td>-52.0</td>\n","      <td>-38.0</td>\n","      <td>-14.0</td>\n","      <td>8.0</td>\n","      <td>24.0</td>\n","      <td>33.0</td>\n","      <td>30.0</td>\n","      <td>13.0</td>\n","      <td>1.0</td>\n","      <td>-12.0</td>\n","      <td>-16.0</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>-41.0</td>\n","      <td>-40.0</td>\n","      <td>-39.0</td>\n","      <td>-76.0</td>\n","      <td>-104.0</td>\n","      <td>-111.0</td>\n","      <td>-95.0</td>\n","      <td>-81.0</td>\n","      <td>-98.0</td>\n","      <td>-94.0</td>\n","      <td>-99.0</td>\n","      <td>-83.0</td>\n","      <td>-105.0</td>\n","      <td>-117.0</td>\n","      <td>-134.0</td>\n","      <td>-165.0</td>\n","      <td>-172.0</td>\n","      <td>-161.0</td>\n","      <td>-143.0</td>\n","      <td>-95.0</td>\n","      <td>-79.0</td>\n","      <td>-92.0</td>\n","      <td>-150.0</td>\n","      <td>-184.0</td>\n","      <td>-215.0</td>\n","      <td>-211.0</td>\n","      <td>-211.0</td>\n","      <td>-210.0</td>\n","      <td>-198.0</td>\n","      <td>-182.0</td>\n","      <td>-167.0</td>\n","      <td>-145.0</td>\n","      <td>-133.0</td>\n","      <td>-147.0</td>\n","      <td>-124.0</td>\n","      <td>-112.0</td>\n","      <td>-89.0</td>\n","      <td>-72.0</td>\n","      <td>-67.0</td>\n","      <td>-65.0</td>\n","      <td>...</td>\n","      <td>-163.0</td>\n","      <td>-141.0</td>\n","      <td>-130.0</td>\n","      <td>-184.0</td>\n","      <td>-216.0</td>\n","      <td>-227.0</td>\n","      <td>-218.0</td>\n","      <td>-172.0</td>\n","      <td>-175.0</td>\n","      <td>-168.0</td>\n","      <td>-159.0</td>\n","      <td>-179.0</td>\n","      <td>-165.0</td>\n","      <td>-171.0</td>\n","      <td>-144.0</td>\n","      <td>-133.0</td>\n","      <td>-148.0</td>\n","      <td>-168.0</td>\n","      <td>-185.0</td>\n","      <td>-175.0</td>\n","      <td>-169.0</td>\n","      <td>-154.0</td>\n","      <td>-144.0</td>\n","      <td>-85.0</td>\n","      <td>-43.0</td>\n","      <td>-22.0</td>\n","      <td>-2.0</td>\n","      <td>-21.0</td>\n","      <td>-20.0</td>\n","      <td>-69.0</td>\n","      <td>-126.0</td>\n","      <td>-146.0</td>\n","      <td>-148.0</td>\n","      <td>-143.0</td>\n","      <td>-140.0</td>\n","      <td>-100.0</td>\n","      <td>-77.0</td>\n","      <td>-42.0</td>\n","      <td>-35.0</td>\n","      <td>-70.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 4096 columns</p>\n","</div>"],"text/plain":["      0      1      2      3      4     ...   4091   4092   4093   4094   4095\n","0   -148.0 -155.0 -156.0 -156.0 -150.0  ...   19.0   21.0   24.0   30.0   38.0\n","1     45.0   52.0   79.0  117.0  146.0  ...  -73.0  -56.0  -55.0  -48.0  -64.0\n","2    -20.0   -3.0    2.0   16.0   29.0  ...   -8.0    6.0   22.0   24.0   21.0\n","3    -28.0  -20.0  -18.0  -22.0  -23.0  ...  -12.0  -16.0   -6.0   -7.0  -13.0\n","4     23.0  144.0  228.0  260.0  255.0  ... -139.0   28.0  140.0  240.0  294.0\n","..     ...    ...    ...    ...    ...  ...    ...    ...    ...    ...    ...\n","145  -55.0  -48.0  -48.0  -38.0  -23.0  ...   54.0   81.0  106.0  130.0  142.0\n","146  -39.0  -27.0  -16.0   -3.0   17.0  ...    4.0    2.0    2.0    0.0   -5.0\n","147   40.0   60.0   74.0   70.0   67.0  ... -109.0 -119.0 -146.0 -138.0 -135.0\n","148  -91.0 -116.0  -70.0    5.0   45.0  ...   30.0   13.0    1.0  -12.0  -16.0\n","149  -41.0  -40.0  -39.0  -76.0 -104.0  ... -100.0  -77.0  -42.0  -35.0  -70.0\n","\n","[150 rows x 4096 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rLU3z78P1VNC","executionInfo":{"status":"ok","timestamp":1619871109350,"user_tz":-330,"elapsed":812,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"fabe2700-814d-48f1-cae1-15b9166bb809"},"source":["print(type(X_test_2048))\n","X_test_2048=pd.DataFrame.to_numpy(X_test_2048)\n","print(type(X_test_2048))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","<class 'numpy.ndarray'>\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Ev7xfxqD05_s"},"source":["X_test_2048 = X_test_2048.reshape([X_test_2048.shape[0], timesteps, data_dim])\n","y_test=np_utils.to_categorical(y_test, num_classes=2)\n","#print(X_test_2048)\n","print(X_test_2048.shape)\n","print(y_test.shape)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Mtdc79MKDvJL"},"source":["# (B.2) Testing model on T=23.6/2 interval"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":246},"id":"7aka1TdJTptG","executionInfo":{"status":"error","timestamp":1620216355865,"user_tz":-330,"elapsed":12120,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"37424718-459d-4020-9aac-2079dfbd023a"},"source":["from keras.models import model_from_json\n","\n","# load json and create model\n","json_file = open('/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.json', 'r')\n","loaded_model_json = json_file.read()\n","json_file.close()\n","loaded_model = model_from_json(loaded_model_json)\n","# load weights into new model\n","loaded_model.load_weights(\"/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.h5\")\n","print(\"Loaded model from disk\")\n"," \n","# evaluate loaded model on test data\n","loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","score = loaded_model.evaluate(X_test_2048,y_test, verbose=1)\n","print(\" %.2f%%\" % ( score[1]*100))\n","\n"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Loaded model from disk\n"],"name":"stdout"},{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-3883ccd0ea01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# evaluate loaded model on test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mloaded_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'adam'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msensitivity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspecificity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloaded_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_2048\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" %.2f%%\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'sensitivity' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"6tnoppXDD8Nh"},"source":["# (B.3) Testing model on T=23.6/4 interval"]},{"cell_type":"code","metadata":{"id":"xDXDMD1r3kVk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619875312707,"user_tz":-330,"elapsed":33942,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"9cb3520c-6ec7-40db-81fe-94db2999fb26"},"source":["#dataset of size 150*(4096/4)\n","print(\"shape of X_test: \",X_test.shape)\n","df_1024=X_test[:,0:1024].copy()\n","print(\"shape of df_2048: \",df_1024.shape)\n","\n","\n","X_test_1024 = pd.concat([pd.DataFrame(df_1024), pd.DataFrame(df_1024),pd.DataFrame(df_1024),pd.DataFrame(df_1024)], axis=1,  ignore_index=True)\n","print(\"\\nshape of df_1024: \",df_1024.shape)\n","print(df_1024)\n","\n","X_test_1024=pd.DataFrame.to_numpy(X_test_1024)\n","\n","X_test_1024 = X_test_1024.reshape([X_test_1024.shape[0], timesteps, data_dim])\n","y_test=np_utils.to_categorical(y_test, num_classes=2)\n","\n","from keras.models import model_from_json\n","# load json and create model\n","json_file = open('/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.json', 'r')\n","loaded_model_json = json_file.read()\n","json_file.close()\n","loaded_model = model_from_json(loaded_model_json)\n","# load weights into new model\n","loaded_model.load_weights(\"/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.h5\")\n","print(\"Loaded model from disk\")\n","\n","\n","# evaluate loaded model on test data\n","loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","score = loaded_model.evaluate(X_test_1024,y_test, verbose=1)\n","print(\" %.2f%%\" % ( score[1]*100))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["shape of X_test:  (60, 4096)\n","shape of df_2048:  (60, 1024)\n","\n","shape of df_1024:  (60, 1024)\n","[[  56.   55.   38. ...  -68.  -73.  -64.]\n"," [ 176.  186.  189. ...  -89. -113. -103.]\n"," [  13.   30.   42. ...   -9.  -34.  -22.]\n"," ...\n"," [-113. -185. -269. ... -295. -400. -393.]\n"," [-133. -144. -113. ... -127. -132. -140.]\n"," [ -12.    6.   13. ...   -6.   -4.  -14.]]\n","Loaded model from disk\n","2/2 [==============================] - 33s 108ms/step - loss: 0.0012 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000\n"," 100.00%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"1nZT3mxjGJ0l"},"source":["# (B.4) Testing model on T=23.6/8 interval"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SSZOWmvXFpEW","executionInfo":{"status":"ok","timestamp":1619876062270,"user_tz":-330,"elapsed":3071,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"fabaf622-a2ea-4763-d233-d043124cc7fe"},"source":["#dataset of size 150*(4096/8)\n","print(\"shape of X_test: \",X_test.shape)\n","df_512=X_test[:,0:512].copy()\n","print(\"shape of df_2048: \",df_512.shape)\n","\n","\n","X_test_512 = pd.concat([pd.DataFrame(df_512), pd.DataFrame(df_512),pd.DataFrame(df_512),pd.DataFrame(df_512) , pd.DataFrame(df_512), pd.DataFrame(df_512),pd.DataFrame(df_512),pd.DataFrame(df_512)], axis=1,  ignore_index=True)\n","print(\"\\nshape of df_512: \",df_512.shape)\n","print(df_512)\n","\n","X_test_512=pd.DataFrame.to_numpy(X_test_512)\n","\n","X_test_512 = X_test_512.reshape([X_test_512.shape[0], timesteps, data_dim])\n","y_test=np_utils.to_categorical(y_test, num_classes=2)\n","\n","from keras.models import model_from_json\n","# load json and create model\n","json_file = open('/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.json', 'r')\n","loaded_model_json = json_file.read()\n","json_file.close()\n","loaded_model = model_from_json(loaded_model_json)\n","# load weights into new model\n","loaded_model.load_weights(\"/content/drive/MyDrive/mtech_finalyr_project/2class problem/model_2class_ABCDvsE.h5\")\n","print(\"Loaded model from disk\")\n","\n","\n","# evaluate loaded model on test data\n","loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","score = loaded_model.evaluate(X_test_512,y_test, verbose=1)\n","print(\" %.2f%%\" % ( score[1]*100))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["shape of X_test:  (60, 4096)\n","shape of df_2048:  (60, 512)\n","\n","shape of df_512:  (60, 512)\n","[[  56.   55.   38. ...  -98. -130. -111.]\n"," [ 176.  186.  189. ...  237.  233.  231.]\n"," [  13.   30.   42. ...  -46.  -56.  -65.]\n"," ...\n"," [-113. -185. -269. ...  -58.  -78.  -28.]\n"," [-133. -144. -113. ...  -40.  -45.  -51.]\n"," [ -12.    6.   13. ...   36.   36.   13.]]\n","Loaded model from disk\n","2/2 [==============================] - 1s 108ms/step - loss: 0.0147 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000\n"," 100.00%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FL8-eWe8H3Tm","executionInfo":{"status":"ok","timestamp":1619876070628,"user_tz":-330,"elapsed":972,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"3dc2f366-f9b0-486e-8ba6-8945415f1da0"},"source":["print(X_test_512.shape)\n","\n","print(y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(60, 2048, 2)\n","(60, 2)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"aM6Mb1uVIBw_"},"source":[""],"execution_count":null,"outputs":[]}]}